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This paper presents a novel method to determine rate-distortion optimized transform coefficients for efficient compression of videos generated from point clouds. The method exploits a generalized frequency selective extrapolation approach that iteratively determines rate-distortion-optimized coefficients for all basis functions of two-dimensional discrete cosine and sine transforms. The method is applied to blocks containing both occupied and unoccupied pixels in video based point cloud compression for HEVC encoding. In the proposed algorithm, only the values of the transform coefficients are changed such that resulting bit streams are compliant to the V-PCC standard. For all-intra coded point clouds, bitrate savings of more than 4% for geometry and more than 6% for texture error metrics with respect to standard encoding can be observed. These savings are more than twice as high as savings obtained using competing methods from literature. In the randomaccess case, our proposed method outperforms competing V-PCC methods by more than 0.5%. [ABSTRACT FROM AUTHOR]

The discrete cosine transform (DCT) is a relevant tool in signal processing applications, mainly known for its good decorrelation properties. Current image and video coding standards—such as JPEG and HEVC—adopt the DCT as a fundamental building block for compression. Recent works have introduced low-complexity approximations for the DCT, which become paramount in applications demanding real-time computation and low-power consumption. The design of DCT approximations involves a trade-off between computational complexity and performance. This paper introduces a new multiparametric transform class encompassing the round-off DCT (RDCT) and the modified RDCT (MRDCT), two relevant multiplierless 8-point approximate DCTs. The associated fast algorithm is provided. Four novel orthogonal low-complexity 8-point DCT approximations are obtained by solving a multicriteria optimization problem. The optimal 8-point transforms are scaled to lengths 16 and 32 while keeping the arithmetic complexity low. The proposed methods are assessed by proximity and coding measures with respect to the exact DCT. Image and video coding experiments and hardware realization are performed. The novel transforms perform close to or outperform the current state-of-the-art DCT approximations. [ABSTRACT FROM AUTHOR]

In image forensics, quantization step estimation plays a crucial role in revealing the JPEG compression history. This study focuses on the estimation for images that have been JPEG compressed and re-saved in lossless formats. Several effective methods have been developed, whereas the performance on small sized images still needs to be improved. In this paper, a novel JPEG quantization step estimation method with low complexity and high efficiency is proposed. First, for a given decompressed image, as the special shape of its DCT coefficients distribution is highly related to the quantization step, a function of the candidate step taking a similar shape is designed. Then, the quantization step is determined as the candidate leading to the maximum response of the designed function on the probability density function of DCT coefficients. The relation between the maximum response and the quantization step is verified mathematically, which provides a theoretical basis for the proposed method. In addition, to further improve the estimation performance, two fine adjustment procedures are adopted. Experimental results demonstrate that the proposed method outperforms some state-of-the-art works, especially on small sized images. [ABSTRACT FROM AUTHOR]

Although significant progress has been achieved recently in automatic learning of steganographic cost, the existing methods designed for spatial images cannot be directly applied to JPEG images which are more common media in daily life. The difficulties of migration are mainly caused by the characteristics of the $8\times 8$ DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. Following a domain-transition design paradigm, the policy network is composed of three modules, i.e., pixel-level texture complexity evaluation module, DCT feature extraction module, and mode-wise rearrangement module. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with $8\times 8$ DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature-based and modern CNN-based steganalyzers. [ABSTRACT FROM AUTHOR]

Joint photographic experts group (JPEG) compression is widely used in image processing and computer vision. Detecting double compressed JPEG images is a common problem in forensics and detecting compressed images with the same quantization matrix remains a challenging task. However, most existing methods were designed for detection in grayscale images and cannot fully use the unique characteristics of color images (such as the relationship between channels and color information). In addition, the performance of existing methods is unsatisfactory for low JPEG quality factors and in cross detection experiments. To solve these problems, we analyze the stability of a color image to obtain the convergence error and transposition error. According to the convergence characteristics of color JPEG images, the continuous compression by the same quantization matrix can make the JPEG image tend to be stable. The final stable state and the convergence process are determined by the number of compressions of the original image. Thus, continuously compressed JPEG images can be regarded as a continuous frame to obtain the convergence error. As the color image converges, its ability to resist interference decreases. To reflect the changes in anti-interference ability, the transposition operation is used to disturb the color JPEG image to obtain the transposition error. In addition, quaternion mapping is used to retain the relationship between continuously compressed JPEG images and enlarge the influence caused by transposition operation. In our experiments on several image databases, the proposed method outperforms existing methods in different settings. [ABSTRACT FROM AUTHOR]

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Passive video forensics has become an active topic in recent years. Generally, a pristine video obtained from surveillance cameras or other video recording devices is single compressed. At the same time, double lossy compression will certainly be introduced in tampered videos since it needs to go through recompression to perform tampering on a commonly used compressed video. In the video’s double compression, some traces are left due to the intrinsic effects of recompression. Traditional supervised learning is inefficient because it requires two classes (the pristine and the manipulated) that occur in the video to be exhaustively assigned labels. Actually, compared with pristine videos, manipulated videos with labels are more difficult to obtain. To address this problem, in this paper, one-class classification, which is often used for anomaly detection and only needs the target class, is introduced. We first treat all decompressed video frames as still images and extract subtractive pixel adjacency matrix (SPAM) steganalysis features to detect traces left in the double compression process. Then we adopt a Gaussian density-based one-class classifier since SPAM features extracted from pristine video frames approximately subject to the Gaussian distribution. Furthermore, we improve the robustness of the classifier by using ensemble strategy. Experimental results indicate that our proposed method exceeds other more complex one-class classification methods, and outperforms fully-supervised learning methods only by feeding features from single compressed video frames to our one-class classifier. [ABSTRACT FROM AUTHOR]

JPEG-domain enhancement improves the visual quality of JPEG images by directly manipulating the decoded DCT (discrete cosine transform) coefficients, which inevitably leads to mixed compression and enhancement artifacts. Existing forensic methods that merely consider JPEG artifacts are unsuitable to address such mixed artifacts and hence suffer a considerable performance decline in compression parameter estimation and lack the ability to estimate the enhancement parameter. This work attempts to explore the characterization of the mixed artifacts, and to further estimate both the enhancement and compression parameters of JPEG-domain enhanced images. First, a statistical likelihood function is proposed to characterize the periodicity of DCT coefficients, which can measure how well an enhanced image is de-enhanced back to its JPEG compressed version given the compression and enhancement parameters. The proposed likelihood function reaches its maximum if the parameters match their true values. Then, a forensic method of enhancement detection and parameter estimation is developed based on the proposed likelihood function for two kinds of classical JPEG-domain enhancement. Specifically, JPEG-domain enhanced images are detected by thresholding a scalar feature computed upon the likelihoods, and the enhancement and compression parameters are estimated by locating the maximal likelihood. In addition, mathematical proof of the de-enhancement feasibility is provided. Experimental results demonstrate that the proposed method outperforms the compared methods in both enhancement detection and parameter estimation. [ABSTRACT FROM AUTHOR]

The emergence of 3D point clouds (3DPCs) is promoting the rapid development of immersive communication, autonomous driving, and so on. Due to the huge data volume, the compression of 3DPCs is becoming more and more attractive. We propose a novel and efficient color attribute compression method for static 3DPCs. First, a 3DPC is partitioned into several sub-point clouds by color distribution analysis. Each sub-point cloud is then decomposed into a lot of 3D blocks by an improved k-d tree-based decomposition algorithm. Afterwards, a novel virtual adaptive sampling-based sparse representation strategy is proposed for each 3D block to remove the redundancy among points, in which the bases of the graph transform (GT) and the discrete cosine transform (DCT) are used as candidates of the complete dictionary. Experimental results over 10 common 3DPCs demonstrate that the proposed method can achieve superior or comparable coding performance when compared with the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel perceptual image hashing scheme based on convolutional neural network (CNN) with multiple constraints is proposed, in which our deep hashing network learns the process of features extraction automatically according to the training target and then generates the final hash sequence. The combination of convolutional and pooling layers is to reduce the size of input image while deepening the channels. Then, we construct two pairs of constraints and integrate them into an overall constraint function through a strategy of weight allocation. In order to guarantee the robustness and discrimination of deep hashing network simultaneously, a new training method is developed to adjust the training set structure dynamically according to the changes of constraint values. Experimental results show that the proposed deep hashing network can achieve a satisfactory balance between perceptual robustnzess and discrimination while maintaining security. Based on the large-scale test set, receiver operating characteristic (ROC) curves, ${F}_{1}$ scores and equal error rate (EER) demonstrate the superiority of our scheme in terms of content authentication compared with some state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In the past decade, the development of transform coding techniques has achieved significant progress and several advanced transform tools have been adopted in the new generation Versatile Video Coding (VVC) standard. In this paper, a brief history of transform coding development during VVC standardization is presented, and the transform coding tools in the VVC standard are described in detail together with their initial design, incremental improvements and implementation aspects. To improve coding efficiency, four new transform coding techniques are introduced in VVC, which are namely Multiple Transform Selection (MTS), Low-Frequency Non-separable Secondary Transform (LFNST) and Sub-Block Transform (SBT), as well as a large (64-point) type-2 DCT. The experimental results on VVC reference software (VTM-9.0) show that average 4.5% and 3.6% overall coding gain can be achieved by the VVC transform coding tools for All Intra and Random Access configurations, respectively. [ABSTRACT FROM AUTHOR]

Reversible Data Hiding (RDH) in JPEG images is valuable for many applications, such as archive management and image authentication. Recently, there emerged a lot of related works for JPEG RDH, however, the current methods utilize the histogram-shifting-based framework in a constant distortion metric, which does not consider the property of DCT coefficients whose modification distortion is miscellaneous with respect to DCT frequency. In this paper, we propose a novel JPEG RDH scheme under multi-distortion metric. At first, the modification distortion is defined as the impact in the spatial domain caused by modifying DCT coefficients. Different from previous works, here we select coefficients with values ±1, ±2 as cover, which will not cause extra bitrate-expansion and decrease the shift distortion. By minimizing both the modification distortion and shift distortion, a heuristic block selecting strategy is proposed. With the selected coefficients and the corresponding distortion, we use recursive histogram modification under the inconsistent-distortion metric for message embedding. The experimental results show that the proposed method can effectively improve visual quality as well as bring small bitstream expansion. Additionally, the undetectability of the proposed method outperforms those of the existing methods. [ABSTRACT FROM AUTHOR]

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security. [ABSTRACT FROM AUTHOR]

Most current reversible data hiding (RDH) techniques are designed for uncompressed images. However, JPEG images are more commonly used in our daily lives. Up to now, several RDH methods for JPEG images have been proposed, yet few of them investigated the adaptive data embedding as the lack of accurate measurement for the embedding distortion. To realize adaptive embedding and optimize the embedding performance, in this article, a novel RDH scheme for JPEG images based on multiple histogram modification (MHM) and rate-distortion optimization is proposed. Firstly, with selected coefficients, the RDH for JPEG images is generalized into a MHM embedding framework. Then, by estimating the embedding distortion, the rate-distortion model is formulated, so that the expansion bins can be adaptively determined for different histograms and images. Finally, to optimize the embedding performance in real time, a greedy algorithm with low computation complexity is proposed to derive the nearly optimal embedding efficiently. Experiments show that the proposed method can yield better embedding performance compared with state-of-the-art methods in terms of both visual quality and file size preservation. [ABSTRACT FROM AUTHOR]

Recently, Li et al. proposed an effective non-additive distortion model for JPEG steganography by preserving Block Boundary Continuity (BBC) in the spatial domain. However, the method based on BBC only explored how the modifications of DCT coefficient pairs at the same mode in adjacent blocks will impact on BBC. In this paper, we propose a method to enhance the BBC, called BBC++, by considering the mutual impact on the BBC from all DCT coefficients in adjacent blocks. To do that, we design updating strategies for both covers and costs in multi-round embedding processions. During the embedding, the cover is updated to repair the BBC after embedding and the corresponding costs are updated to keep the BBC from being destroyed in the next embedding. Experimental results show that the BBC++ can better maintain BBC and outperform previous non-additive distortion steganography when resisting modern JPEG steganalyzers. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework of lossless data hiding (LDH) in JPEG images. The proposed framework contains two algorithms, i.e., the optimized basic LDH and the relay transfer based extension. In the basic algorithm, we aim to preserve the filesize after data embedding. The data hiding process is optimized by variable-length-code (VLC) mapping, combination and permutation. In the extended algorithm, we focus on embedding more bits into the bitstream with a condition that the filesize increment is allowed. To decrease the filesize increment, we propose a relay transfer based algorithm to preprocess the JPEG bitstream. Subsequently, we embed data into the processed bitstream using the basic LDH. Both algorithms provide better performances than previous arts. After lossless data hiding, the marked JPEG bitstream is compliant to common JPEG decoders. Since all operations are implemented on VLCs and the Huffman codes, no distortion is generated on the image pixels. Experimental results demonstrate that the proposed approach outperforms previous methods. [ABSTRACT FROM AUTHOR]

Quantization plays a pivotal role in JPEG compression with respect to the tradeoff between image fidelity and storage size, and the blind estimation of quantization parameters has attracted considerable interest in the fields of image steganalysis and forensics. Existing estimation methods have made great progress, but they usually suffer a sharp decline in accuracy when addressing small-size JPEG decompressed bitmaps due to the insufficiency of coefficients. Aiming to alleviate this issue, this paper proposes a generic clustering-based framework to improve the performance of the existing methods. The core idea is to gather as many coefficients as possible by clustering subbands before feeding them into a step estimator. The proposed framework is implemented using hierarchical clustering with two kinds of histogram-like features. Extensive experiments are conducted to validate the effectiveness of the proposed framework on a variety of images of different sizes and quality factors, and the results show that notable improvements can be achieved. In addition to quantization step estimation, we believe the idea behind the proposed framework might provide inspiration for other forensic tasks to alleviate their performance issues induced by sample insufficiency. [ABSTRACT FROM AUTHOR]

A compressive acquisition technique for on-array image compression is proposed in this paper. It capitalizes on representation ability of accumulated spatial gradients of the acquired scene. The local variations inferred from strength of the accumulated gradients are used as cues to vary number of samples read through the image sensor readout. Such sampling enables the reconstruction using traditional interpolation techniques with desired quality. The proposed method is first verified using MATLAB simulations, where on an average, a compression of 87% is achieved, for a threshold of 40 intensity levels. The images are reconstructed using nearest neighbour interpolation (NNI) method which results in a mean peak signal to noise ratio (PSNR) value of 29.09 dB. The reconstructed images are further enhanced using deep convolutional neural network, which improves the PSNR to 32.46 dB. The biggest advantage of the proposed technique is low-complex hardware design. As a proof of concept, a hardware implementation of the technique is performed using discrete components. Pixel intensity values of standard images are converted into analog voltages using a data acquisition system and mapped in the input voltage range of 1.5 V −5.5 V. For a threshold of 3.8 V, the compression of 81% - 83% is observed for the considered images. The proposed technique is simple and effective, and is suitable for low-power complementary metal oxide semiconductor (CMOS) image sensors. [ABSTRACT FROM AUTHOR]

In terms of feature-based steganalysis for JPEG images, JPEG phase-aware features (e.g., DCTR and GFR) currently provide the best detection performance on modern adaptive steganographic schemes. But in DCTR and GFR, the types of residual images are relatively single. They only use the convolution residuals obtained with the DCT or Gabor filters of fixed size ${8\times 8}$. In this paper, to further improve DCTR and GFR, two strategies are proposed to enrich the features by diversifying residual images, and the corresponding symmetrization rules for the features are also elaborately designed. First, instead of a single filter size of ${8\times 8}$ , convolution filters of multiple sizes are adopted to generate different residual images. Second, we also compute JPEG phase-aware features from the difference images between two convolution residuals. Since the features from convolution residuals and difference images are diverse and complementary, the combination of these two kinds of features can significantly improve the detection accuracy. Last but not least, different symmetrization rules are accordingly designed for these features by considering filter types, filter sizes, and residual subtraction to decrease the feature dimension and enhance the feature robustness. The experimental results demonstrate the effectiveness of our proposed features, and we can further boost the performance by incorporating the knowledge of the selection channel and using the accelerated weighted histogram method. [ABSTRACT FROM AUTHOR]

The future video coding standard named Versatile Video Coding (VVC) is expected by the end of 2020. VVC will enable better coding efficiency than the current High Efficiency Video Coding (HEVC) standard. This coding gain is brought by several coding tools. The Multiple Transform Selection (MTS) is one of the key coding tools that have been introduced in VVC. The MTS concept relies on three transform types including Discrete Cosine Transform (DCT)-II, Discrete Sine Transform (DST)-VII and DCT-VIII. Unlike the DCT-II that has fast computing algorithms, the DST-VII and DCT-VIII rely on more complex matrix multiplication. In this paper an approximation approach is proposed to reduce the computational cost of the DST-VII and DCT-VIII. The approximation consists in applying adjustment stages, based on sparse block-band matrices, to a variant of DCT-II family mainly DCT-II and its inverse. Genetic algorithm is used to derive the optimal coefficients of the adjustment matrices. Moreover, an efficient hardware implementation of the forward and inverse approximate transform module is proposed. The architecture design includes a pipelined and reconfigurable forward-inverse DCT-II core transform as it is the main core for DST-VII and DCT-VIII computations. The proposed 32-point 1D architecture including low cost adjustment stages allows the processing of a video in 2K and 4K resolutions at 1095 and 273 frames per second, respectively. A unified 2D implementation of forward-inverse DCT-II, approximate DST-VII and DCT-VIII is also presented. The synthesis results show that the design is able to sustain a video in 2K and 4K resolutions at 386 and 96 frames per second, respectively, while using only 12% of Alms, 22% of registers and 30% of DSP blocks of the Arria10 SoC platform. [ABSTRACT FROM AUTHOR]

This article investigates the relative performance of multiple data embedding into H.264 compressed video under two schemes of closed-loop and open-loop methods. In closed-loop, a part of an encoder is modified to embed data within the encoding loop during re-compression, while in the open-loop scenario, a part of decoder is modified to embed data out of decoding loop. It is shown, while for the first instance of embedding, both methods behave almost similarly for all picture types, for later instances of embedding, open-loop outperforms the closed-loop method. Moreover, the required time for embedding and extracting processes of open-loop method is only 1-1.5% of that of the closed-loop counterpart. In both methods, the quality of watermarked video and the data hiding capacity are controlled by the position of last non-zero (LNZ) coefficient in the H.264 zigzag scanning order. However, for B-pictures, the side-effect of embedding distortion is very limited (in the order of 0.002 in terms of SSIM), but for I- and P-pictures it can be significant. Picture degradations in I- and P-frames are alleviated by confining data embedding only into the last block of macroblocks of I-frames and the last P-frame in the GOP, respectively. Finally, it is shown that while with CAVLC type entropy coder, the number of increased bits due to data embedding can vary from 5%-65% of the metadata volume (depending on quality degradation) this value with CABAC coder is only less than 20% of that under CAVLC. [ABSTRACT FROM AUTHOR]

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they are unable to handle color images as the compression processes for gray scale and color images are different. Moreover, these methods train a specific model for each compression quality, and they require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with a pixel position labeling map and quantization tables as inputs. We proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN; the implicit dual-domain translation allows the IDCN to handle color images with discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was also developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance; further, it demonstrates great potential for practical applications. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC) involves variable size higher order Discrete Cosine Transform (DCT) to increase coding efficiency. However, higher order DCT increases the hardware complexity, thereby increasing the area and power consumption of the physical design. In this paper, a new method to derive higher order DCT is presented. The proposed method uses rotation based structures and uses trigonometric relations to decompose those structures. Finally, all the floating point coefficients of the rotation unit are approximated by dyadic values such that they can be realized by add and shift operation. Hence, the proposed method eliminates the need for multipliers and significantly reduces the complexity of the core transform for HEVC. The proposed approach is used to design all size of DCT supported by HEVC and can be further extended to design larger size DCT and IDCT to be used in the near future for image and video compressions. The proposed architecture has a maximum throughput of 3.2 Gsps and is capable of processing at least 30 frames/s of ultra high definition $3840\times 2160$ video. Experimental results demonstrate that the proposed design method maintains similar coding efficiency, but requires 24% less area-delay product and 25% less power compared to the HEVC reference algorithm. [ABSTRACT FROM AUTHOR]

Discrete Cosine Transform (DCT) is a commonly used building block for image and video compression. In this article, we present a Markov Random Field (MRF)-based design for DCT implementation because MRF logic gates outperform standard non-MRF units by achieving high noise immunity for applications to logic-based computing systems in deep sub-micron condition. Furthermore, it is found that stochastic logic, a low-cost form of number representation, can also efficiently simplify computations. By combining these two techniques, we present an improved DCT hardware circuit. The example eight-point one-dimensional DCT (1D DCT) system is simulated using 65 nm CMOS technology. Simulation results show that the proposed MRF design can achieve 13% higher noise immunity and 47% area saving, compared with the typical stochastic 1D DCT using classical Master-and-Slave architecture. While achieving the same error rate of 0.21, power consumption is reduced by 52%. [ABSTRACT FROM AUTHOR]

Conventional scalable compressive video sampling cannot sufficiently exploit the structured sparsity within video sequences. This paper proposes a novel quality scalable structured compressive video sampling (SS-CVS) framework with hierarchical subspace learning to support video transmission over heterogeneous network conditions. The proposed framework incorporates the union of data-driven subspaces (UoDS) model to introduce structured sparsity into sensing matrix for progressive acquisition of measurements. Hierarchical subspace learning is developed to generate the subspaces with their bases based on adaptive subspace clustering in a progressive fashion. Consequently, two hierarchical structures are derived to enable progressive mapping of subspaces and bases on the structured sensing matrices to support quality scalability. To guarantee the convergence of hierarchical subspace learning, the training set is updated with the adaptive group set from the reconstructed reference frames. It is demonstrated that SS-CVS can guarantee stable recovery for each quality layer under the constraints of block restricted isometric property (RIP). Furthermore, the proposed SS-CVS model is generalized to tensor subspaces for scalable compressive sampling of high-dimensional signals. Experimental results demonstrate that the proposed algorithm can achieve quality scalabilities with an improved reconstructed performance in comparison to the state-of-the-art scalable compressive video sampling methods. [ABSTRACT FROM AUTHOR]

Among various methods of reversible data hiding (RDH) in JPEG images, only rate-distortion, i.e. the image quality with given payload, is taken into consideration during algorithm designing. However, file size expansion is another important evaluation metric for JPEG RDH methods. Based on this situation, we propose a JPEG RDH method considering both the rate-distortion and the file size expansion at the same time while designing the algorithm. The multi-objective optimization strategy is utilized to realize the balance of the two objectives. Specifically, the cover signal is divided into several non-overlapping parts firstly, and after that, the embedding costs of each part are calculated. Next, the optimized combination of parts for embedding data is gained by multi-objective optimization. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods in terms of rate-distortion and file size expansion performance. [ABSTRACT FROM AUTHOR]

This paper presents a hardware architecture for $8\times 8$ 2D Discrete Cosine Transform (DCT) and Inverse DCT (IDCT) using Taylor-series expansion of trigonometric functions. The processing of DCT/IDCT is modified to eliminate the need for: 1) partitioning the input image into fixed-size blocks and 2) use of transpose buffer for storing intermediate results, achieving low clock cycle time compared to existing techniques. The values obtained from Taylor-series are approximated by fixed-point numbers that reduce the hardware complexity at the cost of acceptable loss in quality of the output image. Based on the fixed-point approach, 18-bit, 24-bit, and 32-bit DCT and IDCT architectures are proposed. This DCT architecture considers the implementation of all 64 coefficients and hence, it is named as DCT64. An important observation about 2D DCT is that most of the compaction energy is concentrated on the low-frequency DCT coefficients which are sufficient to reconstruct the original image. Taking this point into consideration, three DCT architectures, namely, DCT15, DCT10, and DCT6 with 15, 10, and 6 coefficients are designed which are compared with the DCT64. The percentage improvement in the area and performance utilization is obtained for DCT15, DCT10, and DCT6 against DCT64. A gradual increase in the percentage is observed from DCT15 through DCT6 with a marginal decline in the quality of the output image at each step which can be seen from the measured PSNR and SSIM values. Regarding IDCT, the proposed architecture achieved a low percentage of area utilization and similar performance to that of the DCT. The field-programmable gate array (FPGA) implementation of the proposed DCT architecture operates at a higher frequency, achieving lower values of dynamic power, slice reg, slice LUTs metrics that outperforms the processing capabilities of existing Algebraic Integer (AI) and fixed-point implementations. [ABSTRACT FROM AUTHOR]

Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on “Robust Domain Constructing + RS-STC Codes,” combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance. [ABSTRACT FROM AUTHOR]

Robustness is the most important factor that limits the practical application of reversible watermarking. To deal with this issue, several robust reversible watermarking (RRW) techniques have been proposed. Among them, the two-stage RRW framework proposed by Coltuc et al. is a promising one. In the first state of this framework, a robust watermark is embedded into the cover image to provide robustness, and then in the second stage, the information enabling revert the robust embedding is reversibly embedded into the already marked image to guarantee the reversibility. However, because of using the same area for these two embedding stages, the robustness in the first stage is seriously weakened by the reversible embedding. As a result, this elegant method is not effective as expected. Based on this consideration, this paper proposes an independent embedding domain (ED)-based two-stage RRW. The cover image is first transformed into two independent EDs, and then the robust and reversible watermarks are embedded into each domain separately. The carrier derived from the first embedding stage that carrying the robust watermark will not change after the reversible embedding, and thus, the robustness of the first stage is well preserved. By the proposed method, the embedding performance of the original two-stage RRW is significantly enhanced. Moreover, the proposed method is experimentally verified better than some other state-of-the-art RRW methods. [ABSTRACT FROM AUTHOR]

Previous studies have exhibited that incorporating side-information, e.g., a high-quality precover image, can significantly improve steganographic security for JPEG images. This motivates us to estimate the side-information for traditional steganographic scenario in which only a JPEG image is available. It is expected to achieve high-level security by utilizing the estimated side-information similar to side-informed steganography, even though the estimated side-information is not perfectly precise. In this paper, a general framework of side-information estimated (SIE) JPEG steganography is proposed, under which the core problems are how to better estimate the precover and modulate the distortion function correspondingly. To address the two problems, we test several denoising filters and a deblocking filter to obtain the estimated precover, and we introduce two implementation models for modulating the costs. We finally recommend the combination of the deblocking filter and the modulation model using the polarity of the estimated rounding error. The experimental results show that the proposed method dramatically improves the existing additive distortions for images of an arbitrary quality factor and outperforms the state-of-the-art methods based on estimating side-information when resisting modern steganalysis. [ABSTRACT FROM AUTHOR]

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and the simulation results show that the proposed method can achieve the best system performance. [ABSTRACT FROM AUTHOR]

Discrete cosine transform (DCT) is an indispensable module in video codecs and is a major part in many video coding standards including the latest high efficiency video coding (HEVC). As the video resolution increases, both transform sizes and the number of transforms increase continuously which poses challenges to the reusability design especially in hardware implementation. This paper presents reconfigurable transform architecture to flexibly support the reusability of different transform sizes. The proposed architecture maximally reuses the hardware resources by rearranging the order of input data for different transform sizes while still exploiting the butterfly property. Furthermore, this architecture supports reconfigurable throughput according to different hardware resource requirements. By applying the proposed architecture to the field-programmable gate array (FPGA) design of HEVC core transform matrices, the synthesis results show much lower consumption of hardware resources comparing to existing methods in the literature. The implementation in Altera’s Stratix III FPGA can operate at 139 MHz and supports real-time processing of $3840\times 2160$ ultra-high definition video at a minimum of 45 f/s and up to 359 f/s for different DCT sizes. [ABSTRACT FROM AUTHOR]

The rising popularity of social networks and cloud computing has greatly increased a number of JPEG compressed image exchanges. In this context, the security of the transmission channel and/or the cloud storage can be susceptible to privacy leaks. The selective encryption is an efficient tool to mask the image content and to protect confidentiality while remaining format-compliant. However, image processing in the encrypted domain is not a trivial task. In this paper, we present a JPEG crypto-compression method which allows us to recompress a JPEG crypto-compressed image several times, without any information about the secret key or the original image content. Indeed, using the proposed method in this paper, each recompression can be done directly on the JPEG bitstream by removing the last bit of the code representation of each non-zero coefficient, adapting the entropic code part, and slightly modifying the quantization table. This method is efficient to recompress JPEG crypto-compressed images in terms of compression ratio. Moreover, the decryption of the recompressed image produces an image with a very similar visual quality when compared to the original image, according to the obtained results. [ABSTRACT FROM AUTHOR]

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis. [ABSTRACT FROM AUTHOR]

This paper proposes an area-efficient fixed-point architecture for the computation of the discrete cosine transform (DCT) of multiple sizes in high efficiency video coding (HEVC). This result is obtained by comparing different DCT factorizations in order to find the most suitable one for implementation in the HEVC encoder. The recursive structure of fast algorithms, which decompose the $N$ -point DCT by means of two $N/2$ -point DCTs, is exploited to execute computations of small-size DCTs in parallel, thus maximizing the hardware re-usability while maintaining a constant throughput. The simulation results prove that the proposed solution features reduced rate-distortion loss es, with relevant complexity saving compared with the state-of-the-art implementations. Finally, the proposed architecture is exploited to design two families of architectures for the 2D-DCT, namely, folded and full-parallel. [ABSTRACT FROM AUTHOR]

Due to the 3D spatiotemporal regularities of natural videos and small-scale video quality databases, effective objective video quality assessment (VQA) metrics are difficult to obtain but highly desirable. In this paper, we propose a general-purpose no-reference VQA framework that is based on weakly supervised learning with a convolutional neural network (CNN) and a resampling strategy. First, an eight-layer CNN is trained by weakly supervised learning to construct the relationship between the deformations of the 3D discrete cosine transform of video blocks and the corresponding weak labels judged by a full-reference (FR) VQA metric. Thus, the CNN obtains the quality assessment capacity converted from the FR-VQA metric, and the effective features of the distorted videos can be extracted through the trained network. Then, we map the frequency histogram calculated from the quality score vectors predicted by the trained network onto the perceptual quality. Especially, to improve the performance of the mapping function, we transfer the frequency histogram of the distorted images and videos to resample the training set. The experiments are carried out on several widely used VQA databases. The experimental results demonstrate that the proposed method is on a par with some state-of-the-art VQA metrics and has promising robustness. [ABSTRACT FROM AUTHOR]

Hybrid digital–analog (HDA) video transmission schemes have shown advantages in avoiding the cliff effect. However, most current HDA schemes assume perfect transmission of the digital signal, which is hardly the case in practice. In this paper, we propose an adaptive recursive distortion estimate for the HDA system (ARDE-HDA), which recursively estimates the decoder-side distortion from the encoder and adaptively allocates the transmission power between the digital and analog signals in HDA. First, we derive the closed-form expression of a recursive distortion estimation (RDE) method, which does not require the digital part of the HDA output to be decoded perfectly, and both transmission error and superposition process of digital and analog parts are taken into consideration. Then, based on the deduced RDE model, an adaptive power allocation is proposed for the digital and analog parts to minimize the decoder-side distortion. Finally, simulation results are presented, which show the accuracy of the proposed RDE model and the ARDE-HDA method. Our method can achieve an average of 4.10 dB gain over existing HDA methods and 15.14-dB gain over the Softcast method in terms of the peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Transform plays a crucial role in video coding, since it is in the transform domain that the bitrate reduction happens. Recently, coding with transform competition has emerged, especially inside the ITU-T and ISO/MPEG Joint Video Exploration Team, where the adaptive multiple transforms have been thoroughly tested. In this paper, it is proposed to investigate the design of transform competition systems for the purpose of inter-predicted residuals. Several systems are proposed and evaluated in terms of coding efficiency, storage requirement, and encoding complexity. A first approach selects the best transform for a residual block in a pre-established list of transforms. An extended version is also considered, where the number of transforms varies within an image area. Experimental results show that the proposed designs achieve significant bitrate savings over high efficiency video coding from −0.2% to −1.4%. [ABSTRACT FROM AUTHOR]

Posting images on social network platforms is happening everywhere and every single second. Thus, the communication channels offered by various social networks have a great potential for covert communication. However, images transmitted through such channels will usually be JPEG compressed, which fails most of the existing steganographic schemes. In this paper, we propose a novel image steganography framework that is robust for such channels. In particular, we first obtain the channel compressed version (i.e., the channel output) of the original image. Secret data is embedded into the channel compressed original image by using any of the existing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image before the channel transmission (termed the intermediate image), we propose a coefficient adjustment scheme to slightly modify the original image based on the stego-image. The adjustment is done such that the channel compressed version of the intermediate image is exactly the same as the stego-image. Therefore, after the channel transmission, secret data can be extracted from the stego-image with 100% accuracy. Various experiments are conducted to show the effectiveness of the proposed framework for image steganography robust to JPEG compression. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework of reversible data hiding in encrypted JPEG bitstream. We first provide a JPEG encryption algorithm to encipher a JPEG image to a smaller size and keep the format compliant to JPEG decoders. After an image owner uploads the encrypted JPEG bitstreams to cloud storage, the server embeds additional messages into the ciphertext to construct a marked encrypted JPEG bitstream. During data hiding, we propose a combined embedding algorithm including two stages, the Huffman code mapping and the ordered histogram shifting. The embedding procedure is reversible. When an authorized user requires a downloading operation, the server extracts additional messages from the marked encrypted JPEG bitstream and recovers the original encrypted bitstream losslessly. After downloading, the user obtains the original JPEG bitstream by a direct decryption. The proposed framework outperforms previous works on reversible data hiding in encrypted images. First, since the tasks of data embedding/extraction and bitstream recovery are all accomplished by the server, the image owner and the authorized user are required to implement no extra operations except JPEG encryption or decryption. Second, the embedding payload is larger than state-of-the-art works. [ABSTRACT FROM AUTHOR]

Video coding standards are primarily designed for efficient lossy compression, but it is also desirable to support efficient lossless compression within video coding standards using small modifications to the lossy coding architecture. A simple approach is to skip transform and quantization, and simply entropy code the prediction residual. However, this approach is inefficient at compression. A more efficient and popular approach is to skip transform and quantization but also process the residual block in some modes with differential pulse code modulation (DPCM), along the horizontal or vertical direction, prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms can map integer pixels to integer transform coefficients without increasing the dynamic range and can be used for lossless compression. We focus on lossless intra coding and develop novel i2i approximations of the odd type-3 discrete sine transform (ODST-3). Experimental results with the high efficiency video coding (HEVC) reference software show that when the developed i2i approximations of the ODST-3 are used along the DPCM method of HEVC, an average 2.7% improvement of lossless intra frame compression efficiency is achieved over HEVC version 2, which uses only the DPCM method, without a significant increase in computational complexity. [ABSTRACT FROM AUTHOR]

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions. [ABSTRACT FROM AUTHOR]

We present a novel image boundary extension and mean value extension (MVE) for directional lapped transforms, particularly cosine–sine modulated lapped transforms (CSMLTs). Lapped transforms are usually used with an extension technique, such as periodic extension (PE) or symmetric extension (SE), for nonexpansive convolution at signal boundaries. When directional textures (oblique lines and curves) appear at the 2D signal (image) boundaries, both PE and SE produce directional discontinuities, which degrade the sparsity of the transformed coefficients, especially in the case of directional lapped transforms. MVE reduces the discontinuities for directional textures better than PE or SE do; it thus improves the efficiency of the sparse representation based on directional lapped transforms. Moreover, to reduce computational costs compared with those of directional lapped transforms, we introduce new directional block transforms called cosine–sine modulated block transforms. These new transforms are derived from a minimum tile processing (tiling) of $M$ -band CSMLTs with $2M$ filter lengths and nonexpansive convolutions. The resulting directional block transforms, particularly in the case of the MVE, have richer directional selectivity and have better performance compared with a discrete Fourier transform as shown in experiments. [ABSTRACT FROM AUTHOR]

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time. [ABSTRACT FROM AUTHOR]

In this paper, we propose to use a self-adaptive redundant dictionary, consisting of all possible inter and intra prediction candidates, to directly represent the frame blocks in a video sequence. The self-adaptive dictionary generalizes the conventional predictive coding approach by allowing adaptive linear combinations of prediction candidates, which is solved by an rate-distortion aware L0-norm minimization problem using orthogonal least squares (OLS). To overcome the inefficiency in quantizing and coding coefficients corresponding to correlated chosen atoms, we orthonormalize the chosen atoms recursively as part of OLS process. We further propose a two-stage video coding framework, in which a second stage codes the residual from the chosen atoms using a modified discrete cosine transform (DCT) dictionary that is adaptively orthonormalized with respect to the subspace spanned by the first stage atoms. To determine the transition from the first stage to the second stage, we propose a rate-distortion (RD) aware adaptive switching algorithm. The proposed framework is further extended to accommodate variable block sizes ($16\times 16$ , $8\times 8$ , and $4\times 4$), and the partition mode is derived by a fast partition mode decision algorithm. A context-adaptive binary arithmetic entropy coder is designed to code the symbols of the proposed coding framework. The proposed coder shows competitive, and in some cases better RD performance, compared with the HEVC video coding standard for P-frames. [ABSTRACT FROM AUTHOR]

In high efficiency video coding (HEVC) standard, residuals are partitioned into variable-size transform units, and integer cosine transforms (ICTs) of order-4, 8, 16, and 32 are adopted to optimize the coding performance. Order-64 ICT is not used due to its high complexity and limited gain in general coding applications. This paper proposes a set of low-complexity ICTs (LCICTs), from order-8 to order-64, which have fully factorizable structures. LCICTs achieve similar coding performance as the state-of-art methods on HEVC Test Model 13 while requiring lower complexity, e.g., 71% fewer multiplications than the partially factorizable ICTs in the order-64 case. Moreover, we find that order-64 ICTs achieve around 1% average bitrate reduction at high quantization parameters. [ABSTRACT FROM AUTHOR]

Transform, as one of the most important modules of mainstream video coding systems, seems very stable over the past several decades. However, recent developments indicate that bringing more options for transform can lead to coding efficiency benefits. In this paper, we go further to investigate how the coding efficiency can be improved over the state-of-the-art method by adapting a transform for each block. We present a variable block-sized signal-dependent transforms (SDTs) design based on the High Efficiency Video Coding (HEVC) framework. For a coding block ranged from $4\times4$ to $32\times32$ , we collect a quantity of similar blocks from the reconstructed area and use them to derive the Karhunen–Loève transform. We avoid sending overhead bits to denote the transform by performing the same procedure at the decoder. In this way, the transform for every block is tailored according to its statistics, to be signal-dependent. To make the large block-sized SDTs feasible, we present a fast algorithm for transform derivation. Experimental results show the effectiveness of the SDTs for different block sizes, which leads to up to 23.3% bit-saving. On average, we achieve BD-rate saving of 2.2%, 2.4%, 3.3%, and 7.1% under AI-Main10, RA-Main10, RA-Main10, and LP-Main10 configurations, respectively, compared with the test model HM-12 of HEVC. The proposed scheme has also been adopted into the joint exploration test model for the exploration of potential future video coding standard. [ABSTRACT FROM AUTHOR]

In this paper, two metrics for measuring image sharpness are presented and used for an autofocus (AF) application. Both measures exploit reorganized discrete cosine transform (DCT) representation. The first metric is a focus measure, which involves optimal high- and middle-frequency coefficients to evaluate relative sharpness. It is robust to noise while remaining sensitive to the best focus position. A psychometric function-based metric is introduced to quantify the focus measure. The second metric is a no-reference blurriness metric, which is used to measure absolute blurriness. It first constructs multiscale DCT edge maps using directional energy information and then determines image blurriness by combining change information in edge structures with image contrast. This metric gives predictions that are closely correlated with subjective perceived scores and shows performance comparable with that of state-of-the-art methods, especially for noisy images. For noisy situations, the two metrics are adjusted adaptively according to the estimated noise level. To prevent the introduction of extra computational load, an efficient noise-level estimation algorithm based on median absolute deviation is presented. This algorithm exploits only the available reorganized DCT coefficients. With the focus and blurriness measures, an AF method for which the two metrics play an important role was developed. Because of their high-quality performance, the realized AF function is able to locate the best focus position swiftly and reliably. [ABSTRACT FROM PUBLISHER]

This paper proposes a flexible and efficient implementation of the 2D $N$ -point discrete cosine transform (DCT) for the High Efficiency Video Coding (HEVC) standard. The DCT is implemented through the Walsh–Hadamard transform (WHT) followed by Givens rotations. This scheme is exploited to derive an adaptive algorithm, which allows computing of four different approximations ranging from the complete DCT to the WHT, by selectively skipping some rotations. This paper shows the statistical analysis of the DCT usage and derives a precomputation mechanism to adaptively skip rotations. Each approximation, referred to as a operating mode, is characterized by a large saving of operations, at the expense of very small quality loss. Then, two 2D-DCT architectures are proposed: the first one is totally unfolded, while the second one is folded. The two designs are finally synthesized with a 90-nm standard-cell library for a clock frequency of 250 MHz. Both architectures support real-time processing of 8K UHD video sequences at 64 and 26 fps, respectively, and show higher throughput and lower gate count compared with the state-of-art implementations. Moreover, power saving ranging from 28% to 56% can be achieved by working within the proposed operating modes. [ABSTRACT FROM PUBLISHER]