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Storage space and security concerns on multimedia images have emerged as a global issue in recent years. Image encryption algorithm via compressed sensing (CS) is an effective method for data security and reducing storage space. However, the existing CS-based image encryption still faces problems, such as weak resistance to attacks and extensive data storage. We design a high-compression image encryption algorithm that combines fractal and semi-tensor product compressed sensing. First, a measurement matrix required for CS is generated using fractal blocks combined with the semi-tensor product method, which enhances security while reducing the size of the measurement matrix. Then, the measurements obtained from the sampling are used to define the product features of their mean and standard deviation. Exclusion criteria are set, and fractal codes are obtained through matched searching. Finally, the fractal code undergoes scrambling and diffusion, providing triple-layer protection and further improving the security of the secret image. In comparison to conventional methods, our proposed method has greatly improved the compression efficiency through compressed sampling and has the advantages of better concealment and enhanced robustness. Experiments show that we substantiate the effectiveness and superior performance of our method, all while upholding image quality and security. [ABSTRACT FROM AUTHOR]

Image compression plays a vital role in various applications, such as the storage, transmission, and sharing of digital images. We present a symmetric image compression that leverages improved normalization and attention mechanisms to achieve superior compression performance. We focus on addressing the limitations of conventional normalization techniques in handling diverse image characteristics effectively. An adaptive normalization module is proposed to adjust normalization parameters dynamically based on the content of the input image; it ensures optimal data representation and contributes to improved compression efficiency. Furthermore, a windowed attention mechanism is integrated into the compression network to selectively focus on significant image regions while suppressing noise and redundancies by effectively capturing and preserving important visual features to enhance the overall compression quality and reconstruction fidelity. Comparative analysis with state-of-the-art methods demonstrates the superiority of the proposed symmetric image compression network in terms of compression ratio, reconstruction quality, and subjective visual perception. Moreover, the effects of different normalization and attention configurations on compression performance are thoroughly investigated and analyzed. The results of the experiments validate the effectiveness of the proposed symmetric image compression network with improved normalization and windowed attention, known as the Image Compression Attention Normalization Network. The integration of adaptive normalization and windowed attention mechanisms not only enhances the compression efficiency but also enables adaptability to diverse image characteristics. The proposed compression scheme results are as follows. The inference time for encoding and decoding is 0.152 and 0.163 s, respectively. The Bjøntegaard delta (BD) rate shows a reduction of 74.8936%, and the BD-peak signal-to-noise ratio increases by 6.425 dB. The computational load is measured in floating-point operations and totals 263.614G, with the model utilizing 68.1M parameters. Windowed attention preserves critical data, and adaptive normalization alters image processing, enhancing ablation studies. Despite differences, both modules contribute to emphasizing data retention and adjustments and optimizing image processing for improved results. [ABSTRACT FROM AUTHOR]

With the recent advances in high computing capabilities and reliable delivery networks, it is realistic to expect emerging immersive media applications such as virtual reality (VR) and 360-deg video. ISO/IEC Motion Picture Experts Group (MPEG) has launched an MPEG-I project that focuses on not just the current but also the next-generation immersive media applications. Light field (LF) is one of the candidates under consideration by MPEG-I. A LF lenslet image contains plentiful repeating patterns since each microlens captures the same scene. On the other hand, intrablock copy (IBC) in screen content coding (SCC) increases the coding efficiency of screen content (SC) by finding the repeating patterns. SCC can be an efficient encoder for LF image compression. However, SCC is not optimized for compressing lenslet images. Therefore, we propose an efficient lenslet image coding (LIC) model using SCC to encode lenslet images based on the lenslet image characteristics. Experimental results show that our LIC model further improves the coding efficiency of SCC on lenslet images by 3.72% on average and up to 7.77% in bitrate reduction. For fast approach, LIC is faster than SCC by 44.12%. With our proposed LIC model, the encoded bitstreams are compliant with the SCC standard with an even faster decoding time. [ABSTRACT FROM AUTHOR]

This study examines the lossless compression of an original JPEG bitstream such that spare space is reserved to embed secret data in a file header. After an original image is transformed using discrete cosine transform (DCT), the nonzero DCT coefficients at high frequencies are relatively sparse and their absolute values are small. To improve encoding efficiency, we propose a segment encoding algorithm that defines a high frequency in DCT blocks as the termination point and recodes coefficients separately. The coefficients above this point are encoded in the proposed Rxy code, and the coefficients below it are encoded in JPEG code. Then, to further preserve the embedding capacity's (EC) robustness to different quality factors, we cluster blocks into large 32  ×  32 blocks and record special block indexes that are not suitable for Rxy encoding in appended data. Hence, space is reserved for inserting secret data and appended data after the comment marker in a file header. A receiver can obtain the termination point through data analysis, extract additional data, and recover the original JPEG image in a lossless manner. Encoding redundancy is substantially reduced such that a large EC can be achieved with no file expansion. Moreover, only coefficients at high frequencies are recoded; hence, concealment is computationally simple. Experimental results demonstrate that the proposed method has a larger EC than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Among the variety of multimedia formats, color images play a prominent role. A technique for lossless compression of color images is introduced. The technique is composed of first transforming a red, green, and blue image into luminance and chrominance domain (YC[sub u]C[sub v]). Then, the luminance channel Y is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced earlier, Burrows-Wheeler inversion coder or JPEG 2000 is used in compression of those C[sub u] and C[sub v] channels. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes, such as CALIC, M-CALIC, Better Portable Graphics, JPEG-LS, JPEG 2000, and the previously proposed hierarchical prediction and context-adaptive coding technique LCIC. [ABSTRACT FROM AUTHOR]

Sharpness is an important indicator to evaluate image quality or to optimize parameters in computer vision tasks, such as image acquisition, compression, and restoration. We utilize difference quotients to construct an absolute difference quotient and a relative difference quotient to evaluate the sharpness among images containing difference contents and the sharpness among pixels in the same image, respectively. Based on the constructed quotients, we estimate the pixel sharpness index and the image block sharpness index and create a single sharpness index as the overall sharpness of an image by pooling strategy. Our quotient-based methods can assess image sharpness effectively and efficiently. Experimental results on four simulated databases with real blurring and synthetic blurring images show the proposed sharpness metric is consistent with subjective sharpness evaluations and is competitive with existing sharpness metrics. It achieves a balance between running time and high performance.© 2019 spie and is&t [doi: 10.1117/1.JEI.28.1.013032] Keywords: sharpness assessment; blurriness assessment; difference quotients; image quality. Paper 180565 received Jun. 26, 2018; accepted for publication Jan. 7, 2019; published online Feb. 12, 2019. [ABSTRACT FROM AUTHOR]

A shadow-removal algorithm based on cycle generative adversarial network is proposed. There are two networks in the proposed method. To increase the diversity of shadow images for improving the robustness in the shadow-removal process, the first network is used to add shadows in nonshadow images to increase the variation of training data. Then, the second network is trained for the shadow-removal task. Six different losses are calculated and combined in the loss function to increase the performance. Ablation experiments show that the resulting images suffer from some artifacts without any of the six losses in the loss function. The proposed method presents lower value of root-mean-squared error and the superior visual quality compared to the state-of-the-art image removal algorithms.© 2019 spie and is&t [doi: 10.1117/1.JEI.28.1.013034] [ABSTRACT FROM AUTHOR]

Although learned image compression methods have achieved competitive rate-distortion performances, learned video compression remains challenging. The current mainstream learned video compression frameworks usually improve the motion prediction module to reduce the redundancy in video sequences. Although these methods can achieve a great compression ratio, they often ignore the improvement in the entropy model, which does not fully exploit the temporal and spatial characteristics in the video. Meanwhile, these methods always suffer from the error propagation problem. To solve those problems, we propose a learned video compression framework with channel-wise autoregressive entropy model. Our framework captures spatial–temporal dependencies through a powerful entropy model to reduce the redundancy in video sequences. In particular, we do not directly compress the frames in the pixel domain, with no need for the motion prediction module, avoiding the error propagation problem. To better utilize the temporal contexts of the previous frame, we propose the window temporal prior module. Experiments show that our proposed video compression framework achieves promising compression effects in terms of peak signal-to-noise ratio and multiscale structural similarity. [ABSTRACT FROM AUTHOR]

The widely adopted video stabilization techniques in imaging devices have become a bottleneck that limits the performance of source camera identification (SCI) based on photo-response non-uniform (PRNU) noise. Affine transformations in the stabilized video, including scaling, translation, and rotation, introduce varying degrees of distortion to the PRNU pattern across frames, which lead to a degradation of accuracy and prolonged matching time in SCI methods. To address these issues, we propose a stabilized video source identification method that combines the undecimated dual-tree complex wavelet transform (UDTCWT) with the Bayesian adaptive direct search (BADS) algorithm. First, decoded frames are obtained by skipping the loop filter in the video codec to preserve more PRNU. Then, a PRNU extraction algorithm based on the UDTCWT is utilized to filter out more reliable PRNU from the decoded frames. Finally, the distorted PRNU is optimized and recovered by the affine transformation model with the BADS. Experiments on public datasets show that the proposed method outperforms state-of-the-art methods in terms of matching speed and accuracy. [ABSTRACT FROM AUTHOR]

How to achieve a robust performance remains an intractable problem with the various object tracking algorithms, due to some unfavorable factors, e.g., occlusion, appearance change, etc. A robust object-tracking approach is proposed based on a composite similarity measure. Based on the first several frames in a video sequence, an initial dictionary is constructed by searching for the best candidates via the kd-tree method. For a target candidate, a likelihood value is computed by weighting the sparse representation coefficients of local patches obtained via the adaptive structural local sparse appearance (ASLSA) model. In order to alleviate the effect of occlusion, a smoothing operation is designed in computing the likelihood value. Meanwhile, after obtaining the codebook by applying the k-means clustering algorithm on the randomly sampled positive and negative templates, a confidence value is computed, via the sparse collaborative model (SCM), for each of the candidates. Furthermore, a composite similarity measure is devised, which combines the likelihood value and the confidence value for each candidate. Finally, the best candidate is searched for, according to the composite similarity measure. Due to the complementarity of SCM and ASLSA, the proposed algorithm is a more robust approach to object tracking. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method. ©2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.4.043053] [ABSTRACT FROM AUTHOR]

A method of fast coding mode selection for high-efficiency video coding (HEVC) intravideo coding to exclude from the testing procedure coding modes that are statistically rarely chosen by the HEVC encoder is presented. To achieve the best compromise between computational complexity and compression performance, detailed conditional statistics of modes are used in the method. The proposed method enables (1) fast partitioning of each coding tree unit into coding unit, prediction unit (PU), and transform unit blocks of a proper size and (2) faster selection of an appropriate intrapredictor for each PU block. Due to the proposed method, it is possible to significantly scale down the computational complexity of the encoder. Simulation results indicate that, in the case of 1920 χ 1080 sequences, there is a threefold and twofold reduction in the encoding time, while the BD-rate increases by 4% and 1.5%, respectively, compared with HEVC test model version 16.9 of the reference software. Considering the principle of the proposed method, it is evident that it can complement other proposals concerning fast mode selection in the HEVC video encoder. ©2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.4.043051] [ABSTRACT FROM AUTHOR]

Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We train our models on the DIV2K dataset and evaluate their performance on public benchmark datasets. To validate the generality and universality of the proposed method, we created and utilized a new dataset, called WIN143, for over-processed images evaluation. Experimental results indicate that our proposed approach outperforms other CNN-based methods and achieves state-of-the-art performance. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.27.4.043037] [ABSTRACT FROM AUTHOR]

This paper proposes an automatic target recognition of synthetic aperture radar image by joint classification of multiresolution representations with discrimination analysis. The multiresolution representations are generated to better convey the inner discriminability of the original image. Considering the extended operating conditions (EOCs) in practical applications, it is preferred that those resolutions, which make little contributions to correct target recognition, are not used. Accordingly, the discrimination analysis is performed to select the resolutions with notably high discriminability for the present operating conditions based on their reliability levels calculated from sparse representation-based classification. Afterward, the selected resolutions are jointly classified based on joint sparse representation for target recognition. Finally, extensive experiments are conducted on the moving and stationary target acquisition and recognition dataset under both the standard operating condition and various EOCs. The results validate the effectiveness and robustness of the proposed method. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.4.043030] [ABSTRACT FROM AUTHOR]

We report the results of a set of psychophysical experiments that measure the perceptual strengths of videos with different combinations of blockiness, blurriness, and packet-loss artifacts. Participants were instructed to search each video for impairments and rate the strength of their individual features (artifacts). A repeated-measure analysis of variance (RM-ANOVA) performed on the data showed that artifact physical strengths have a significant effect on annoyance judgments. We tested a weighted Minkowski model, a support vector regression model, and a linear model on the experimental data. We found that all these models give a good description of the relation between individual artifact perceptual strengths and the overall annoyance. In the other words, all models presented a very good correlation with the experimental data, showing that annoyance can be modeled as a multidimensional function of the individual artifact perceptual strengths. Additionally, results show that there are interactions among artifact signals. ©2018 SPIE and IS&T [DDI: 10.1117/1.JEI.27.4.043018] [ABSTRACT FROM AUTHOR]

Perceptual quality assessment of a three-dimensional (3-D) image is one of the most important tasks in various applications such as 3-D image coding, processing, enhancement, and monitoring system. But objective quality assessment of 3-D images is still a challenging task. Especially, blind quality assessment of 3-D images encounters an arduous challenge due to lack of prior information about the original images. To solve this problem, we propose a blind 3-D images' quality evaluator by simulating binocular rivalry and orientation responses of the human visual system. As a main technical contribution of this research, both the low-and high-level binocular rivalry responses (BRR), as well as binocular orientation-tuned (BOT) responses, are considered for blind quality assessment of 3-D images. Specifically, the self-similarity of the BRR and BOT responses is extracted from the distorted 3-D images, which will change in the presence of distortions. Subsequently, all quality-aware features are mapped to subjective quality scores of the distorted 3-D images via using support vector regression. The performance of our algorithm is evaluated over two popular LIVE 3-D phase I and phase II databases and shown to be competitive with the state-of-the-art algorithms. ©20i8SPiEand IS&T [DOI: 10.1117/1 .JEI.27.4.043016] [ABSTRACT FROM AUTHOR]

This paper proposes a saliency detection method based on the aggregated Wasserstein distance. A multidimensional Gaussian mixture model is used to model the superpixels, whereby the color information of different color spaces is combined. To overcome the lack of the closed-form solution for the Gaussian mixture model, we employ the aggregated Wasserstein distance to measure the perceptual similarity between different superpixels. The saliency value is then calculated from two aspects. First, the global saliency is computed through all the superpixels in the image using the aggregated Wasserstein distance. Second, the local saliency is computed in a lower range with the same measure. Finally, a saliency map is obtained by combining the two types of saliencies, and is filtered by spectral clustering all the superpixels. The experimental results show that the proposed method outperforms 11 recent exact algorithms on three widely used open datasets. ©2018SPIE and IS&T [DOI: 10.1117/1.JEI.27.4.043014] [ABSTRACT FROM AUTHOR]

Digital image capture, processing, storage, transmission, and display are now taken for granted as part of the technology of modern everyday life. Digital image compression is one of the enabling technologies of the present multimedia world. The image compression technique used for application as diverse as photography, web pages, medical imaging, and public records is JPEG, named after the ISO/CCITT "joint photographic experts group," established in 1986, which developed the technique in the late 1980s and produced the international standard in the early '90s. ITU-T T.81|ISO/IEC 10918-1, also called "JPEG-1" has become one of the most successful standards in information and communication technologies (ICT) history. The authors of this paper -- all members of the original JPEG development team -- were all intimately involved in image-coding research and JPEG in particular. The paper goes behind the scenes explaining why and how JPEG came about and looks under the bonnet of the technique explaining the different components that give the standard of its efficiency, versatility, and robustness that have made a technique that has stood the test of time and evolved to cover applications beyond its original scope. In addition, the authors give a short outlook of the main milestones in coding schemes of still images since "JPEG-1." [ABSTRACT FROM AUTHOR]

This paper presents a temporal video error concealment method specially designed for H.265/HEVC. We propose the quad-tree partitioning prediction and the coherency sensitive hashing in order for better error concealment performance in the corrupted frames via HEVC codec. First, we try to deduce the most probable partitioning of the missing coding tree unit (CTU) using the proposed quad-tree partitioning prediction, which generates several CUs that constitute the CTU. Then, a coding unit (CU) priority choosing method is applied to select the best one from these CUs for prior concealment. Last, the coherency sensitive hashing is adopted for concealing the chosen best CU for better searching quality. The experiments shows that the recovery performance of the purposed method surpasses the compared state-of-the-art methods since the quad-tree partitioning prediction, the priority choosing process, and the coherency sensitive hashing help to improve the overall performance. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033039] [ABSTRACT FROM AUTHOR]

The quality of screen content images (SCIs) plays a crucial benchmark of displaying the system and directly affects the user experience of multidevice communication. However, accurately predicting the quality of SCIs with an objective algorithm still has many challenges. Hence, we propose a new no-reference method for evaluating the quality of SCIs effectively and efficiently. Specifically, we first propose a simple but effective strategy to partition the SCIs into the rough and smooth regions. Then, a most preferred structure (MPS) metric is derived to represent the meaningful part of each image patch according to the free-energy theory of human brain. Subsequently, the MPSs of the rough and smooth regions are pooled for constructing the rough and smooth features of SCIs, separately. Finally, to evaluate the final visual quality, the rough and smooth features are combined as the quality-aware features of SCIs, and a learning-based framework is employed to map the quality-aware features to objective quality scores. The experimental results show that the performance of the proposed method is highly consistent with the human perception and superior to the state-of-the-art quality assessment methods on the SCI-oriented databases. Furthermore, the proposed method delivers a high computational efficiency and is very suitable to be integrated into the displaying system. © 2018 SPIE and IS&T [DOI: 10.1117/1. JEI.27.3.033025] [ABSTRACT FROM AUTHOR]

With the increasing size of video sequences, the dramatically increased access bandwidth and power consumption for fetching and storing reference frames with external memory during motion estimation become the bottleneck of realizing a practical video codec system. To reduce the access bandwidth and power consumption with no quality loss for reference frames, a lossless reference frame compression method based on pixelwise adaptive prediction is proposed. The algorithm allocates pixels from the first line and first column with horizontal prediction and vertical prediction separately. Meanwhile, two direction coefficients of horizontal and vertical are calculated to evaluate the similarity between two adjacent samples. For pixels in the rest locations, prediction value is assigned to every pixel adaptively from left, upper, and upper left adjacent reconstructed pixels according to the appropriate mode decision scheme based on similarity coefficients. At last, residuals are compressed by Huffman encoding process to generate bit-stream stored into external memory. By implementing the algorithm with high-efficiency video coding (HEVC) reference software HEVC test model-16.5 (HM-16.5), the experimental results show that the method can obtain 63.83% data reduction ratio averagely for luminance component. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033022] [ABSTRACT FROM AUTHOR]

By considering the rate distortion optimization (RDO) characteristics, a multilevel optimal λ decision algorithm for the rate control is proposed. With the process of getting the optimal λ for the group of picture (GoP) level rate control, the frame-level rate control, and the coding tree unit (CTU)-level rate control, a special relationship of the current level λ and the next level λ can be built. First, an approximate linear bit allocation relationship is used between the bit rate and the motion compensation prediction error to assign the bit rate more reasonably for every frame. Second, the multilevel optimal λ decision scheme is proposed. To minimize the whole distortion of a sequence, an effective way is to get the optimal RDO of every GoP. Then, with the RDO process of the GoPlevel rate control, an optimal frame λ ratio for each frame in current GoP can be obtained. For the optimal frame λ ratio, its value is mainly decided by the distortion effect, which is calculated by an employed source distortion propagation chain. Finally, an optimal CTU λ clip scheme is proposed to get the optimal RDO of every CTU. The experimental results show that the coding quality of the proposed rate control algorithm is improved significantly with little loss of the bit rate accuracy. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033003] [ABSTRACT FROM AUTHOR]

An image compression method utilizing multiple sampling-rate downsampling and super-resolution upconversion is proposed. The multiple sampling-rate downsampling modes and different quantization patterns are designed for each 32 × 32 macroblock at the encoder side. Meanwhile, the rate distortion optimization strategy is investigated to obtain the optimal downsampling and quantization mode. The chosen mode is used to downsample and code the original macroblocks. To obtain the full resolution block, the deep learning-based multiple models super-resolution upconversion is designed to reconstruct the decoded block at the decoder side. The experimental results demonstrate that our method can obtain the higher quality compressed image than JPEG and some state-of-the-art downsampling-based methods at almost all bit rates. At the same decoded image quality, our method can achieve 30% to 55% bit savings at low bit rates and 15% to 30% bit savings at medium to high bit rates. In addition, the proposed framework is applicable to other image compression standards. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033001] [ABSTRACT FROM AUTHOR]

Advances in CMOS technology have made high-resolution image sensors possible. These image sensors pose significant challenges in terms of the amount of raw data generated, energy efficiency, and frame rate. This paper presents a design methodology for an imaging system and a simplified image sensor pixel design to be used in the system so that the compressed sensing (CS) technique can be implemented easily at the sensor level. This results in significant energy savings as it not only cuts the raw data rate but also reduces transistor count per pixel; decreases pixel size; increases fill factor; simplifies analog-to-digital converter, JPEG encoder, and JPEG decoder design; decreases wiring; and reduces the decoder size by half. Thus, CS has the potential to increase the resolution of image sensors for a given technology and die size while significantly decreasing the power consumption and design complexity. We show that it has potential to reduce power consumption by about 23% to 65%. [ABSTRACT FROM AUTHOR]

The 3D-high efficiency video coding has introduced tools to obtain higher efficiency in 3-D video coding, and most of them are related to the depth maps coding. Among these tools, the depth modeling mode-1 (DMM-1) focuses on better encoding edges regions of depth maps. The large memory required for storing all wedgelet patterns is one of the bottlenecks in the DMM-1 hardware design of both encoder and decoder since many patterns must be stored. Three algorithms to reduce the DMM-1 memory requirements and a hardware design targeting the most efficient among these algorithms are presented. Experimental results demonstrate that the proposed solutions surpass related works reducing up to 78.8% of the wedgelet memory, without degrading the encoding efficiency. Synthesis results demonstrate that the proposed algorithm reduces almost 75% of the power dissipation when compared to the standard approach. [ABSTRACT FROM AUTHOR]

This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. In addition, we identify potential distracting regions that are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to the state-of-the-art tracking algorithms. [ABSTRACT FROM AUTHOR]

An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted from each image in the pyramid and then concatenated to shape one single descriptor of the image. Third, the procedure is finalized by using decision making based on sparse coding. Experiments conducted on two datasets, composed of 125 and 221 subjects, respectively, have demonstrated the efficiency of the proposed strategy as compared to various existing methods. For instance, scores of 96.27% and 96.93% have been obtained for the datasets, respectively. [ABSTRACT FROM AUTHOR]

A block-level fast coding scheme for depth maps coding in 3D-high efficiency video coding (3D-HEVC) is presented. The proposed scheme uses the information of the correlated texture coding unit (CU) to accelerate the encoding depth map CU. The experimental analysis demonstrates that when the SKIP mode encodes the texture CU, the current depth map CU has a high probability of being encoded by SKIP or depth intraskip mode. Therefore, the evaluation of the remaining encoding modes can be skipped when any of these modes obtained a low rate-distortion cost. Experimental results obtained through 3D-HEVC test model 16.0 report that our scheme reduces the encoding time by 26.9% with encoding efficiency losses lower than 0.3%. [ABSTRACT FROM AUTHOR]

We aim to devise a technique to share k number of secret images to n number of participants with (n ≥ k) in such a way that only k or more participants can collaborate to get the original k secret images. The proposed approach relies on the development of a spanning tree from a complete graph G for the share image generation in which each of the k pixels retrieved from the k secret images is considered to be one of the k vertices of G. Each of the spanning trees of G is represented by a unique sequence of numbers known as Prufer sequences, and each participant is assigned a random Prufer sequence. The Prufer sequence of a participant is used to generate the share images for that participant. The share images are kept in a blockchain using interplanetary file system. This scheme is suitable for protecting sensitive images through distributed storage while eliminating the single points of failure. Because the blockchain is an immutable decentralized public record, anyone can verify their shares as well as the shares of other participants, thereby preventing cheating by any participant. The scheme presents a lossless reconstruction of k secret images, making it perfect for use in military applications, medical applications, and other applications in which lossless recovery is a crucial element. Extensive experimental analysis confirms that the proposed scheme is secure, cheat proof, and lossless. [ABSTRACT FROM AUTHOR]

Reversible data hiding in encrypted images (RDHEI) is a well-established technique in which additional information is embedded to ensure data imperceptibility and full reversibility. For RDHEI, how to utilize more redundant space to improve the embedding capacity (EC) under the premise of true reversibility is still a challenging task. We propose a separable RDHEI scheme based on multiple predictive compression coding. The image is classified by texture complexity based on gray level co-occurrence matrix. For different blocks, adaptive weight prediction (AWP) and global optimization adaptive weight prediction (GOAWP) are proposed to improve prediction accuracy. More importantly, GOAWP solves the problem of inaccurate prediction of texture block. Furthermore, methods based on pixel difference compression, pixel bit-based difference compression, and block plane-based difference compression are proposed to vacate more embedding space. The proposed scheme is separable. For the content owner, above two predictors and three compression methods are adopted to vacate space for hiding. For the data hider, the secret bits are embedded into the encrypted image by directly replacing the spare bits without obtaining any information of the original image. For the receiver, the desired information can be retrieved according to the key in his/her possession. By taking full advantage of the pixel correlation and multiple predictive compression coding, the proposed method successfully achieves a significant improvement in capacity. Experimental results show that the highest payload of our scheme reaches 3.6185 bpp, which exhibits appreciated EC in comparison with state-of-the-art methods. Security and compression performance is emphasized, where the key space of our scheme is as high as 2 × 1015 × ∞ × 1015, the average information entropy of the embedded compressed image is 7.8891, and the correlation coefficients between original images and the compressed and embedded compressed images are close to 0, indicating a weak correlation, thereby greatly improving security. [ABSTRACT FROM AUTHOR]

Growing reliance on digital communications has necessitated development of dependable and secure technologies to ensure that the transmission and reception of images over the Internet do not pose a risk to the data of individuals and governments. We propose developing a hybrid image encryption and compression algorithm by combining compressive sensing, the gray wolf algorithm, and multi-dimensional chaotic systems. It aims to generate a highly secure encrypted image while conserving transmission and storage resources. This algorithm overlaps several stages designed to protect vital images while minimizing size. First, the image is converted to the frequency domain using the discrete wavelet transform. Then, the discrete wavelet transform coefficients are scrambled globally using the Waleed-Ali Map and the gray wolf algorithm. Second, the confused image is measured by a parameters-controlled matrix to reduce transmission costs. The final encrypted image is obtained after performing the diffusion operation with a bitstream derived from the Nahrain chaotic map. The average peak signal-to-noise ratio score was 53.1995, and the average mean squared error score was 0.6130, demonstrating that the plaintext and decrypted images are identical. The average correlation coefficient score was −0.010095; the average entropy analysis was 7.9987; and the average number of pixel change rate and unified average changing intensity analyses were 99.60 and 33.52, respectively. The experimental results demonstrate the algorithm's efficiency and robustness, as well as the high quality of the reconstructed image. [ABSTRACT FROM AUTHOR]

With the popularization of high-definition (HD) images and videos (1920 × 1080 pixels and above), there are even 4K (3840 × 2160) television signals and 8 K (8192 × 4320) ultrahigh-definition videos. The demand for HD images and videos is increasing continuously, along with the increasing data volume. The storage and transmission cannot be properly solved only by virtue of the expansion capacity of hard disks and the update and improvement of transmission devices. Based on the full use of the coding standard high-efficiency video coding (HEVC), super-resolution reconstruction technology, and the correlation between the intra- and the interprediction, we first put forward a “division-compensation”-based strategy to further improve the compression performance of a single image and frame I. Then, by making use of the above thought and HEVC encoder and decoder, a video compression coding frame is designed. HEVC is used inside the frame. Last, with the super-resolution reconstruction technology, the reconstructed video quality is further improved. The experiment shows that by the proposed compression method for a single image (frame I) and video sequence here, the performance is superior to that of HEVC in a low bit rate environment. [ABSTRACT FROM AUTHOR]

Motivated by the principle of coded-aperture imaging and microwave radar coincidence imaging (RCI), terahertz radar coded-aperture imaging (RCAI) is proposed to obtain high-resolution, forward-looking, and staring imaging. The terahertz RCAI method resolves target scatterers using the correlation processing of the modulated echoes and the reference signal matrix. This process is similar to the basic principle of RCI. To investigate the coding strategies of terahertz RCAI, this study derives three signal models describing operation with the coded aperture placed separately in the transmitting terminal and the receiving terminal and in both of them simultaneously. Furthermore, coding strategies are categorized according to coded-aperture placement and encoded signal, including amplitude modulation and phase modulation. In addition, qualitative and quantitative analysis methods are introduced to describe the resolving ability of terahertz RCAI. Numerical simulations are performed to analyze and compare the performance on different coding strategies. These analyses can provide constructive guidelines for the design of terahertz RCAI systems. [ABSTRACT FROM AUTHOR]

Existing image compression and encryption methods have several shortcomings: they have low reconstruction accuracy and are unsuitable for three-dimensional (3D) images. To overcome these limitations, this paper proposes a tensor-based approach adopting tensor compressive sensing and tensor discrete fractional random transform (TDFRT). The source video images are measured by three key-controlled sensing matrices. Subsequently, the resulting tensor image is further encrypted using 3D cat map and the proposed TDFRT, which is based on higher-order singular value decomposition. A multiway projection algorithm is designed to reconstruct the video images. The proposed algorithm can greatly reduce the data volume and improve the efficiency of the data transmission and key distribution. The simulation results validate the good compression performance, efficiency, and security of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a unified one-dimensional (1-D) coding framework of image and video, which depends on deep learning neural network and image patch clustering. First, an improved K-means clustering algorithm for image patches is employed to obtain the compact inputs of deep artificial neural network. Second, for the purpose of best reconstructing original image patches, deep linear autoencoder (DLA), a linear version of the classical deep nonlinear autoencoder, is introduced to achieve the 1-D representation of image blocks. Under the circumstances of 1-D representation, DLA is capable of attaining zero reconstruction error, which is impossible for the classical nonlinear dimensionality reduction methods. Third, a unified 1-D coding infrastructure for image, intraframe, interframe, multiview video, three-dimensional (3-D) video, and multiview 3-D video is built by incorporating different categories of videos into the inputs of patch clustering algorithm. Finally, it is shown in the results of simulation experiments that the proposed methods can simultaneously gain higher compression ratio and peak signal-to-noise ratio than those of the state-of-the-art methods in the situation of low bitrate transmission. [ABSTRACT FROM AUTHOR]

This paper proposes a joint image encryption and compression scheme based on a new hyperchaotic system and curvelet transform. A new five-dimensional hyperchaotic system based on the Rabinovich system is presented. By means of the proposed hyperchaotic system, a new pseudorandom key stream generator is constructed. The algorithm adopts diffusion and confusion structure to perform encryption, which is based on the key stream generator and the proposed hyperchaotic system. The key sequence used for image encryption is relation to plain text. By means of the second generation curvelet transform, run-length coding, and Huffman coding, the image data are compressed. The joint operation of compression and encryption in a single process is performed. The security test results indicate the proposed methods have high security and good compression effect. [ABSTRACT FROM AUTHOR]

Authenticity is one of the most important evaluation factors of images for photography competitions or journalism. Unusual compression history of an image often implies the illicit intent of its author. Our work aims at distinguishing real uncompressed images from fake uncompressed images that are saved in uncompressed formats but have been previously compressed. To detect the potential image JPEG compression, we analyze the JPEG compression artifacts based on the tetrolet covering, which corresponds to the local image geometrical structure. Since the compression can alter the structure information, the tetrolet covering indexes may be changed if a compression is performed on the test image. Such changes can provide valuable clues about the image compression history. To be specific, the test image is first compressed with different quality factors to generate a set of temporary images. Then, the test image is compared with each temporary image block-byblock to investigate whether the tetrolet covering index of each 4 × 4 block is different between them. The percentages of the changed tetrolet covering indexes corresponding to the quality factors (from low to high) are computed and used to form the p-curve, the local minimum of which may indicate the potential compression. Our experimental results demonstrate the advantage of our method to detect JPEG compressions of high quality, even the highest quality factors such as 98, 99, or 100 of the standard JPEG compression, from uncompressed- format images. At the same time, our detection algorithm can accurately identify the corresponding compression quality factor. [ABSTRACT FROM AUTHOR]

The removal of rain streaks is critical for cameras in rainy weather conditions. Traditional single-image-based methods suffer from the defects of losing detail information and being time consuming. This study presents a single-image-based rain streak removal using multidimensional variational mode decomposition (MVMD). Rainy image is decomposed using MVMD into several subimages with specific directional and frequency characteristics. Thus, the rain streak components will be limited to one or two subimages, which are called rainy subimages. Rainy subimages are determined based on the directional characteristics. The sub-images are narrow band-limited. Therefore, the rainy subimages contain few outline and detail information with other frequency and directional characteristics. Thus, the derain procedure that is performed on the rainy sub-images will preserve most of the detailed information. The rain components are removed from the rainy sub-images by a bilateral filter. The rain components can be successfully removed from the rainy image through the summation of the subimages without rain components and the filtered rainy subimages. The experimental results show that the proposed method has good rain removal capability while retaining most of the details with a lower running time compared with other existing methods. [ABSTRACT FROM AUTHOR]

Intraprediction has been significantly improved in high efficiency video coding over H.264/AVC with quad-tree-based coding unit (CU) structure from size 64x64 to 8x8 and more prediction modes. However, these techniques cause a dramatic increase in computational complexity. An intracoding algorithm is proposed that consists of perceptual fast CU size decision algorithm and fast intraprediction mode decision algorithm. First, based on the visual saliency detection, an adaptive and fast CU size decision method is proposed to alleviate intraencoding complexity. Furthermore, a fast intraprediction mode decision algorithm with step halving rough mode decision method and early modes pruning algorithm is presented to selectively check the potential modes and effectively reduce the complexity of computation. Experimental results show that our proposed fast method reduces the computational complexity of the current HM to about 57% in encoding time with only 0.37% increases in BD rate. Meanwhile, the proposed fast algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality. [ABSTRACT FROM AUTHOR]

The purpose of image steganalysis is to determine whether the carrier image contains hidden information or not. Since JPEG is the most commonly used image format over social networks, steganalysis in JPEG images is also the most urgently needed to be explored. However, in order to detect whether secret information is hidden within JPEG images, the majority of existing algorithms are designed in conjunction with the popular computer vision related networks without considering the key characteristics of image steganalysis. It is crucial that the steganographic signal, as an extremely weak signal, can be enhanced during its representation learning process. Motivated by this insight, we introduce a representation learning algorithm for JPEG steganalysis that is mainly consisting of a graph attention learning (GAL) module and a feature enhancement module. The GAL module is designed to avoid global feature loss caused by the local feature learning of convolutional neural network and reliance on depth stacking to extend the perceptual domain. The feature enhancement module is applied to prevent the stacking of convolutional layers from weakening the steganographic information. In addition, pretraining as a way to initialize the network weights with a large-scale dataset is utilized to enhance the ability of the network to extract discriminative features. We advocate pretraining with ALASKA2 for the model trained with BOSSBase + BOWS2. The experimental results indicate that the proposed algorithm outperforms previous arts in terms of detection accuracy, which has verified the superiority and applicability of the proposed work. [ABSTRACT FROM AUTHOR]

In the current state-of-the-art distributed video coding (DVC) solutions, side information (SI) frames are created by motion-compensated interpolation. This is the same technique used by frame rate upconversion operations. Recently, these operations are accomplished using deep learning (DL), with significantly improved outcomes. DL is also used to improve the decoded images of numerous compression methods. Consequently, it is natural to wonder what are the impacts of these enhancements on DVC. In this regard, we employed DL networks to create SI and enhance the quality of the DVC decoded frames. We show how these two operations were applied to DVC and their effects on rate-distortion (RD) performance. The comparison with state-of-the-art DVC systems shows an improvement in the quality of the SI frames and that of the decoded frames, and thus an improvement of the RD performance of DVC. [ABSTRACT FROM AUTHOR]

The conventional image compression framework is pixel fidelity-driven, which can generate compressed images with considerable visual quality even at low bit rates. However, these methods emphasize the human visual experience and ignore the need for machine recognition-driven tasks. To this end, we propose an image compression framework that utilizes multiscale prior information extracted from the machine perceptual model to improve the machine recognition accuracy of compressed images. Specifically, the interaction refinement module (IRM) is designed to interact multiscale prior information with each other, adaptively retaining machine recognition–relevant features to enhance its expression on compact features. To further improve the accuracy of machine recognition, machine vision perceptual loss is designed on semantic variation weight, which is the weight of semantic variation degree of deep adjacent layers in multiscale priors. Machine vision perceptual loss is used to optimize the semantic distortion of compressed images for retaining important semantic information. Experimental results show that compared with compression methods including BPG, WebP, Mentzer, NIC, IUWD, and RCIS, the Top-1 recognition accuracy of the proposed method is improved by 10.9%, 19%, 11.6%, 12.9%, 6%, and 2.7% at a lower bit rate (0.2 bpp). In addition, the performance improvement on other machine recognition networks and machine vision tasks shows the versatility of the proposed method. [ABSTRACT FROM AUTHOR]

Compressive imaging presents the challenge of reconstructing an image accurately from its under-sampling measurements. The total variation model, used as the regularization for imaging reconstruction, ignores structure direction, which tends to result in over-smoothing of homogenous regions and staircase artifacts in textured regions. This paper proposes to solve this problem using a compressive reconstruction method with adaptive directional total variation (DTV) based on the structure orientation field (SOF). This approach builds the estimation model of the structure tensor upon the Huber norm as the penalty term, reducing the degradation influence and reaching a quick solution using the primal-dual algorithm. The method then uses the structure tensor to describe the SOF and to establish compressive reconstruction in which the SOF restricts the DTV regularization. Finally, by analyzing the structure of the objective function, this approach adopts the linearized alternating direction method of multipliers to address the challenges presented by the reconstruction model. The experimental results show that the proposed method requires fewer iterations and demonstrates better reconstruction performance than existing methods, as indicated by examining texture details for visual fidelity and quality as measured through other objective criteria. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.6.063023] [ABSTRACT FROM AUTHOR]

The research in developing steganalytic features has been highly successful. These features are extremely powerful when applied to supervised binary classification problems. However, they are incompatible with unsupervised universal steganalysis because the unsupervised method cannot distinguish embedding distortion from varying levels of noises caused by cover variation. This study attempts to alleviate the problem by introducing similarity retrieval of image statistical properties (SRISP), with the specific aim of mitigating the effect of cover variation on the existing steganalytic features. First, cover images with some statistical properties similar to those of a given test image are searched from a retrieval cover database to establish an aided sample set. Then, unsupervised outlier detection is performed on a test set composed of the given test image and its aided sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called SRISP-aided unsupervised outlier detection, requires no training. Thus, it does not suffer from model mismatch mess. Compared with prior unsupervised outlier detectors that do not consider SRISP, the proposed framework not only retains the universality but also exhibits superior performance when applied to high-dimensional steganalytic features. [ABSTRACT FROM AUTHOR]

A fast measurement matrix based on scrambled block Hadamard ensemble for compressed sensing (CS) of hyperspectral images (HSI) is investigated. The proposed measurement matrix offers several attractive features. First, the proposed measurement matrix possesses Gaussian behavior, which illustrates that the matrix is universal and requires a near-optimal number of samples for exact reconstruction. In addition, it could be easily implemented in the optical domain due to its integer-valued elements. More importantly, the measurement matrix only needs small memory for storage in the sampling process. Experimental results on HSIs reveal that the reconstruction performance of the proposed measurement matrix is comparable or better than Gaussian matrix and Bernoulli matrix using different reconstruction algorithms while consuming less computational time. The proposed matrix could be used in CS of HSI, which would save the storage memory on board, improve the sampling efficiency, and ameliorate the reconstruction quality. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.6.063021] [ABSTRACT FROM AUTHOR]

There are monocular depth cues present in images or videos that aid in depth perception in two-dimensional images or videos. Our objective is to preserve the defocus depth cue present in the videos along with the salient regions during compression application. A method is provided for opportunistic bit allocation during the video compression using visual saliency information comprising both the image features, such as color and contrast, and the defocus- based depth cue. The method is divided into two steps: saliency computation followed by compression. A nonlinear method is used to combine pure and defocus saliency maps to form the final saliency map. Then quantization values are assigned on the basis of these saliency values over a frame. The experimental results show that the proposed scheme yields good results over standard H.264 compression as well as pure and defocus saliency methods. [ABSTRACT FROM AUTHOR]

The visible quality assessment of images is important to evaluate the performance of image processing methods such as image correction, compressing, and enhancement. The structural similarity is widely used to determine the visible quality; however, existing structural similarity metrics cannot correctly assess the perceived human visibility of images that have been slightly geometrically transformed or images that have undergone significant regional distortion. We propose an improved structural similarity metric that is more close to human visible evaluation. Compared with the existing metrics, the proposed method can more correctly evaluate the similarity between an original image and various distorted images. [ABSTRACT FROM AUTHOR]

The sparsifying representation plays a significant role in compressive sensing (CS)-based hyperspectral (HS) imaging. Training the dictionaries for each dimension from HS samples is very beneficial to accurate reconstruction. However, the tensor dictionary learning algorithms are limited by a great amount of computation and convergence difficulties. We propose a least squares (LS) type multidimensional dictionary learning algorithm for CS-based HS imaging. We develop a practical method for the dictionary updating stage, which avoids the use of the Kronecker product and thus has lower computation complexity. To guarantee the convergence, we add a pruning stage to the algorithm to ensure the similarity and relativity among data in the spectral dimension. Our experimental results demonstrated that the dictionaries trained using the proposed algorithm performed better at CS-based HS image reconstruction than those trained with traditional LS-type dictionary learning algorithms and the commonly used analytical dictionaries. [ABSTRACT FROM AUTHOR]

Feature correspondence is one of the essential difficulties in image processing, given that it is applied within a wide range in computer vision. Even though it has been studied for many years, feature correspondence is still far from being ideal. This paper proposes a multigeometric-constraint algorithm for finding correspondences between two sets of features. It does so by considering interior angles and edge lengths of triangles formed by third-order tuples of points. Multigeometric-constraints are formulated using matrices representing triangle similarities. The experimental evaluation showed that the multigeometric-constraint algorithm can significantly improve the matching precision and is robust to most geometric and photometric transformations including rotation, scale change, blur, viewpoint change, and JPEG compression as well as illumination change. The multigeometric-constraint algorithm was applied to object recognition which includes extraprocessing and affine transformation. The results showed that this approach works well for this recognition. [ABSTRACT FROM AUTHOR]

An efficient biprediction decision scheme of high efficiency video coding (HEVC) is proposed for fast-encoding applications. For low-delay video applications, bidirectional prediction can be used to increase compression performance efficiently with previous reference frames. However, at the same time, the computational complexity of the HEVC encoder is significantly increased due to the additional biprediction search. Although a some research has attempted to reduce this complexity, whether the prediction is strongly related to both motion complexity and prediction modes in a coding unit has not yet been investigated. A method that avoids most compression-inefficient search points is proposed so that the computational complexity of the motion estimation process can be dramatically decreased. To determine if biprediction is critical, the proposed method exploits the stochastic correlation of the context of prediction units (PUs): the direction of a PU and the accuracy of a motion vector. Through experimental results, the proposed method showed that the time complexity of biprediction can be reduced to 30% on average, outperforming existing methods in view of encoding time, number of function calls, and memory access. [ABSTRACT FROM AUTHOR]