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Deep learning has achieved great success in the past decade, especially in the fields of computer vision and image processing. After witnessing such success, video coding experts are motivated to consider whether deep learning can also benefit video coding, and if so, they seek to discover why and how. Indeed, a number of research studies have been conducted to explore deep learning for image and video coding, which has been an active and fast-growing research area especially since the year 2015. These prior arts can be divided into two categories: new coding schemes that are built solely upon deep networks (deep schemes), and deep network-based coding tools that are embedded into traditional coding schemes (deep tools). Moreover, in the responses to the joint call for proposals on video compression with capability beyond High Efficiency Video Coding (HEVC), a number of deep tools have been proposed, and some of them are further studied for the upcoming Versatile Video Coding (VVC). In this paper, we summarize the ongoing efforts in the Joint Video Experts Team about the proposed deep tools, and we discuss several promising tools in much detail, including neural network-based intra prediction, convolutional neural network (CNN) based in-loop filtering, and CNN-based block-adaptive-resolution coding. A series of experimental results are provided to demonstrate the capability of these tools in achieving higher compression efficiency than the VVC or HEVC anchor. These results shed light on the promising direction of deep learning-based future video coding, towards which a lot of open problems call for further study. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant progress in the research and development of the high dynamic range (HDR) video technology and the state-of-the-art video pipelines are able to offer a higher bit depth support to capture, store, encode, and display HDR video content. In this paper, we introduce a novel HDR video compression algorithm, which uses a perceptually uniform color opponent space, a novel perceptual transfer function to encode the dynamic range of the scene, and a novel error minimization scheme for accurate chroma reproduction. The proposed algorithm was objectively and subjectively evaluated against four state-of-the-art algorithms. The objective evaluation was conducted across a set of 39 HDR video sequences, using the latest x265 10-bit video codec along with several perceptual and structural quality assessment metrics at 11 different quality levels. Furthermore, a rating-based subjective evaluation ($n=40$) was conducted with six sequences at two different output bitrates. Results suggest that the proposed algorithm exhibits the lowest coding error amongst the five algorithms evaluated. Additionally, the rate–distortion characteristics suggest that the proposed algorithm outperforms the existing state-of-the-art at bitrates ≥ 0.4 bits/pixel. [ABSTRACT FROM AUTHOR]

We present a high-quality temporal frame interpolation (TFI) method that employs piecewise-smooth motion and handles disoccluded regions using the observation that motion discontinuities travel with the foreground object. We derive a “motion discontinuity” likelihood map from the divergence of a motion field between the input frames. Motion which is modeled at the reference frame is mapped to the target frame using a cellular-affine mapping strategy—a process during which regions of disocclusion are readily observed. This information is then used to guide the occlusion-aware, bidirectional FI process. Furthermore, we propose two computationally inexpensive texture optimizations that selectively improve the quality of the interpolated frames in regions around moving objects. The scheme produces very high-quality interpolated frames and outperforms current high-quality state-of-the-art TFI schemes by 2–2.5 dB; the method works with a very low-complexity motion estimation scheme and runs orders of magnitudes faster than its competitors. [ABSTRACT FROM AUTHOR]

A residue number system (RNS) has an inherent parallel structure that can be utilized for improving computer hardware systems. An RNS represents large integer numbers as a smaller integer set, or residues of a modulo set, without carry propagation between them. Hence mathematical operations, such as addition or subtraction, can be performed on the residues independently. This paper proposes an RNS implementation of motion estimation for the latest video coding standard known as high-efficiency video coding (HEVC) or H.265. Since motion estimation is the most computationally intensive task in video coding, several simplified algorithms are proposed for mitigating the problem, but the majority of them result in a worsening peak signal-to-noise ratio (PSNR) or bit-rate performance, or sometimes both. This paper also proposes a modified algorithm based on a test-zone (TZ) search algorithm, a widely used fast-search algorithm with good rate-distortion performance, suitable for hardware implementation for encoding ultra-high-definition videos in real time. The results show that worst-case PSNR degradation and bit-rate increases compared with the TZ search in the HEVC reference software implementation are negligible, and the hardware gate count is less than for many other designs in the literature. [ABSTRACT FROM AUTHOR]

We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard data sets shows that the proposed approach is competitive with the best two-stream video classification approaches found in the literature. At the same time: 1) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; 2) selective decoding is up to 12 times faster than full-frame decoding; and 3) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature. [ABSTRACT FROM AUTHOR]

A video compression framework based on spatio-temporal resolution adaptation (ViSTRA) is proposed, which dynamically resamples the input video spatially and temporally during encoding, based on a quantisation-resolution decision, and reconstructs the full resolution video at the decoder. Temporal upsampling is performed using frame repetition, whereas a convolutional neural network super-resolution model is employed for spatial resolution upsampling. ViSTRA has been integrated into the high efficiency video coding reference software (HM 16.14). Experimental results verified via an international challenge show significant improvements, with BD-rate gains of 15% based on PSNR and an average MOS difference of 0.5 based on subjective visual quality tests. [ABSTRACT FROM AUTHOR]

Recently, multiple ($k, n$) region-incrementing VCSs (RIVCSs) have been proposed that can gradually reconstruct secrets in a single image. In ($k, n$)-RIVCS, the secret image is subdivided into multiple regions in such a way that any $t$ shadow images, where $k \le t \le n$ , can be used to reveal the secret in the ($t-k+1$)th region. This region-incrementing property provides progressive decoding. However, the regions in previous RIVCSs are disjointed: no two regions have overlapping areas. In this paper, we discuss a ($k, n$) region-in-region progressive VCS (RPVCS). Our ($k, n$)-RPVCS has ($n-k+1$) secrecy-level regions, and each region can be located within any previous region. This new type of region allocation not only provides more areas in which to hide the secret compared with the non-overlapping regions in RIVCS but also provides different presentation methods in progressive decoding. [ABSTRACT FROM AUTHOR]

Rate control is a key technique for video communication systems. The aim of rate control is to transmit the best possible quality video sequences under various restrictions, such as channel bandwidth, buffer capacity, maximum time delay allowed for a given service, and so on. The $\lambda $ domain rate control technique ($\lambda $ -RC) has been integrated into the latest High Efficiency Video Coding standard (H.265/HEVC) test model, due to its accurate bit estimation and high rate-distortion performance. However, it is found that the $\lambda $ -RC is not the optimal choice under the random-access configuration. When the random-access configuration is used, pictures are organized into temporal layers, where pictures in different layers are of different importance in terms of prediction. In this paper, a picture level lambda domain rate control technique for the random-access configuration in H.265/HEVC is proposed. The influence of temporal layers is effectively considered in the proposed algorithm referred to as TL- $\lambda $ -PRC. Experimental results verify that the proposed TL- $\lambda $ -PRC is efficient in coding performance and accurate in bit estimation. Compared with the $\lambda $ -RC with the fixed ration bit allocation which has been implemented in the test model of H.265/HEVC (HM 14.0), TL- $\lambda $ -PRC achieves an average reduction of 4.10% and 3.49% for slow motion and fast motion sequences, respectively, in BD-rate (Bjøntegaard-Delta bitrate) with more accurate bit estimation. The performance of different algorithms in terms of algorithm complexity and quality fluctuation are also carefully analyzed in this contribution. [ABSTRACT FROM AUTHOR]

Screen content coding plays an important role in many applications. Conventional reference picture management (RPM) strategies developed for natural content may not work well for screen content. This is because many regions in screen content remain static for a long time, causing a lot of repetitive contents to stay in the decoded picture buffer. The repetitive contents are not conducive to inter prediction, but still occupy valuable memory. This paper proposes a diversity-based RPM scheme for screen content coding. The concept of diversity is introduced for the reference picture set (RPS) to help formulate the RPM problem. By maximizing the diversity of RPS, more potentially better predictions are provided. Better compression performance can then be achieved. Meanwhile, the proposed scheme is nonnormative and compatible with existing video coding standards, such as High Efficiency Video Coding. The experimental results show that, for low delay screen content coding, the bit saving of the proposed scheme is 4.9% on average and up to 13.7%, without increasing encoding time. [ABSTRACT FROM AUTHOR]

In this paper, a novel rate-control algorithm based on the region adaptive R- $\lambda $ model is proposed for depth maps coding. First, in order to obtain an accurate rate control for depth maps coding, a modified frame level bit allocation method based on coding bits statistical distribution of depth maps is proposed. Second, considering that different areas in a depth map have an imparity effect on virtual view rendering, the blocks of the depth map are divided into two types, namely, interested blocks for virtual view rending (IBV) and noninterested blocks for virtual view rending (NIBV). Then, two different R- $\lambda $ models are derived for IBV and NIBV, respectively. The optimal bitrates for IBV and NIBV are determined by solving an optimization problem. After that, based on the regional R- $\lambda $ models, the optimal Lagrange multipliers are calculated for both IBV and NIBV. Finally, the largest coding unit (LCU) level rate control is performed by adaptively adjusting the Lagrange multiplier to avoid blocking artifacts and smooth the quality of coding. Experimental results demonstrate that the proposed method can achieve considerable BD-PSNR gains compared with the unified rate-quantization model and conventional R- $\lambda $ model-based algorithms in terms of rendered virtual views quality. [ABSTRACT FROM AUTHOR]

Multiview video plus depth format has been adopted as the emerging 3D video representation recently. It includes a limited number of textures and depth maps to synthesize additional virtual views. Since the quality of depth maps influences the view synthesis process, their sharp edges should be well preserved to avoid mixing foreground with background. To address this issue, 3D-High Efficiency Video Coding (HEVC) introduces new coding tools, a partition-based intra mode [depth modeling mode (DMM)], a residual description technique [segmentwise depth coding (SDC)], and a more complex rate-distortion (RD) evaluation with view synthesis optimization (VSO), to provide more accurate predictions and achieve higher compression rate. However, these new techniques introduce a lot of possible candidates, and each of them requires complicated RD calculation in the process of intra-mode decision. They lead to unacceptable computational burden in a 3D-HEVC encoder. Therefore, in this paper, we raise two efficient techniques for depth intra-mode decision. First, by investigating the statistical characteristics of variance distributions in the two partitions of DMM, a simple but efficient criterion based on the squared Euclidean distance of variances (SEDV) is suggested to evaluate RD costs of the DMM candidates instead of the time-consuming VSO process. Second, a probability-based early depth intra-mode decision is proposed to select only the most promising mode and make the early determination of using SDC based on the low-complexity RD cost in rough mode decision. Experimental results show that the proposed algorithm with these two new techniques provides 33%–48% time reduction with little drop in the coding performance compared with the state-of-the-art algorithms. [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]

With the advance of coding and network technology, the multiview video (MVV) system has received growing attention in the 3-D TV market since it can provide consumers with more realistic scenes from different viewpoints. In the MVV system, each video sequence consists of one color-map video and one depth video to be encoded and the depth-image-based rendering technique is often used to create/synthesize virtual views for different viewpoints. To attain the same quality of virtual views, encoding depth videos in a fast and bitrate-saving manner is crucial. Based on the depth no-synthesis-error model developed by Zhao et al., this paper proposes a new intra-/inter-prediction scheme first and then a fast quadtree structure determination scheme for encoding depth videos in 3-D High Efficiency Video Coding (3D-HEVC). The first proposed scheme has bitrate-saving merit because for each coding unit, each depth value is modified to approach the predicted intra-/inter-depth value as closely as possible, but without causing any synthesis errors in the resultant virtual views. The second proposed scheme has the merit of low computational cost because it can terminate the quadtree structure determination for coding tree units as early as possible. Based on four typical test video sequences, the empirical results demonstrate that on average, our proposed encoding method for depth videos has 16.3% bitrate and 13.8% encoding-time improvement ratios when compared with the traditional method in 3D-HEVC test model, while preserving the quality of the rendered virtual views. In addition, our proposed method outperforms that of Lee et al. in terms of the required bitrate and encoding time. [ABSTRACT FROM AUTHOR]

Several different methods have been investigated in recent years, aiming at computational complexity reduction and scaling of High Efficiency Video Coding (HEVC) software implementations. However, maintaining the encoding time per frame or group of pictures (GOPs) below an adjustable upper bound is still an open research issue. A solution for this problem is devised in this paper based on a set of Pareto-efficient encoding configurations, identified through rate-distortion-complexity analysis. The proposed method combines a medium-granularity encoding time control with a fine-granularity encoding time control to accurately limit the HEVC encoding time below a predefined target for each GOP. It is shown that the encoding time can be kept below a desired target for a wide range of encoding time reductions, e.g., up to 90% in comparison with the original encoder. The results also show that compression efficiency loss (Bjøntegaard delta-rate) varies from negligible (0.16%) to moderate (9.83%) in the extreme case of 90% computational complexity reduction. [ABSTRACT FROM AUTHOR]

To save manufacturing cost, most color digital video cameras employ a single-sensor technology with a red–green–blue (RGB) color filter array (CFA) to capture real-world scenes. Due to only one primary color measured at each pixel location, the captured videos are usually referred to as the mosaic videos. For the purposes of economical storage and transmission, it is very important to achieve a good tradeoff between the quality and bitrate when compressing mosaic videos with different RGB-CFA structures. In this paper, based on mathematical optimization technique, a novel chroma subsampling strategy is presented for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and High Efficiency Video Coding (HEVC). For each $2 \times 2$ YUV block to be subsampled with 4:2:0 format, the proposed strategy determines the proper sampled $U$ and $V$ components by minimizing, prior to compression, the quality distortion between the original colocated mosaic block and the mosaic block conversed from the current subsampled YUV block. Through the mathematical optimization formulated in the proposed strategy, the significance of the sampled $U$ and $V$ components for reconstructing R, G, and B pixels can be simultaneously taken into consideration. The experimental results demonstrate that the proposed chroma subsampling strategy has the best quality and bitrate tradeoff at a similar execution time requirement for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and HEVC compared with the state-of-the-art ones by Chen et al. and Yang et al. as well as the three commonly used ones. [ABSTRACT FROM AUTHOR]

We propose an adaptive hybrid digital–analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital–analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios. [ABSTRACT FROM PUBLISHER]

This paper presents an optimized parallel algorithm for the next-generation video codec High Efficiency Video Coding (HEVC). The proposed method provides maximized parallel scalability by exploiting two levels of parallelism: 1) frame level and 2) task level. Frame-level parallelism is exploited using a graph that efficiently provides a parallel coding order of the frames with complex reference dependencies. The proposed reference dependency graph is generated at runtime by a novel construction algorithm that dynamically analyzes the configuration of the HEVC codec. Task-level parallelism is exploited to provide further scalability to frame-level parallelization. A pipelined execution is allowed for independent tasks, which are defined by dividing and categorizing a single coding process into multiple types of tasks. The proposed parallel encoder and decoder do not suffer from loss in coding efficiency because neither constraints nor modification in coding options are required. The proposed parallel methods result in an average encoding speedup of 1.75 and the aggressive method that exploits additional frame-level parallelism achieved 6.52 speedup using eight physical cores. [ABSTRACT FROM PUBLISHER]

This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding. [ABSTRACT FROM PUBLISHER]

Single instruction multiple data (SIMD) instructions have been commonly used to accelerate video codecs. The recently introduced High Efficiency Video Coding (HEVC) codec like its predecessors is based on the hybrid video codec principle and, therefore, is also well suited to be accelerated with SIMD. In this paper we present the SIMD optimization for the entire HEVC decoder for all major SIMD instruction set architectures. Evaluation has been performed on 14 mobile and PC platforms covering most major architectures released in recent years. With SIMD, up to $5\times $ speedup can be achieved over the entire HEVC decoder, resulting in up to 133 and 37.8 frames/s on average on a single core for Main profile 1080p and Main10 profile 2160p sequences, respectively. [ABSTRACT FROM AUTHOR]

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes. [ABSTRACT FROM PUBLISHER]

This paper addresses high performance depth coding in 3D video by making good use of its coded texture video counterpart. The relationship between the depth and its associated texture video in terms of coding mode and motion vector is carefully examined. Our statistical study suggests that the skip-coding mode and its associated motion vectors in the coded texture can be shared for depth coding by saving bit rate at the cost of little increase of distortion, which subsequently results in a nonsequential coding of the depth map. In this sense, coding/prediction of a block can be performed using the skip-coded blocks below and right, which are not available in the conventional sequential coding, thus producing the so-called omnidirectional blocks predicted in the intra-coding by making the best use of (at most) four neighboring blocks. Moreover, in view of the depth-texture structure similarity, a depth-texture cooperative clustering-based prediction method is proposed for cluster-based depth prediction in the intra-coding, which exploits the structure similarity for the current coding block and its neighboring pixels around the block. On the other hand, some large prediction errors may be present for the depth-texture misaligned pixels, which may greatly compromise the coding performance. To deal with these large residuals induced by the depth-texture misalignment, a simple yet effective detection and rectification approach is incorporated in the proposed depth coding scheme. Experimental results show that our proposed depth coding scheme achieves superior rate-distortion performance compared with other relevant coding methods. [ABSTRACT FROM PUBLISHER]

This paper presents a maximum a posteriori (MAP) framework to incorporate a spatial consistency prior model for depth reconstruction in the shape-from-focus (SFF) process. Existing SFF techniques, which reconstruct a dense 3-D depth from multifocus image frames, usually have poor performance over low-contrast regions and usually need a large number of frames to achieve satisfactory results. To overcome these problems, a new depth reconstruction process is proposed to estimate the depth values by solving an MAP estimation problem with the inclusion of a spatial consistency model. This consistency model assumes that within a local region, the depth value of each pixel can be roughly predicted by an affine transformation of the image features at that pixel. A local learning process is proposed to construct the consistency model directly from the multifocus image sequence. By adopting this model, the depth values can be inferred in a more robust way, especially over low-contrast regions. In addition, to improve the computational efficiency, a cell-based version of the MAP framework is proposed. Experimental results demonstrate the effective improvement in accuracy and robustness as compared with existing approaches over real and synthesized image data. In addition, experimental results also demonstrate that the proposed method can achieve quite impressive performance, even with only the use of a few image frames. [ABSTRACT FROM AUTHOR]

We present a novel approach to free-viewpoint video. Our main contribution is the formulation of a hybrid approach between image morphing and depth-image based rendering. When rendering the scene from novel viewpoints, we use both dense pixel correspondences between image pairs as well as an underlying, view-dependent geometrical model. Our novel reconstruction scheme iteratively refines geometric and correspondence information. By combining the strengths of both depth and correspondence estimation, our approach enables free-viewpoint video also for challenging scenes as well as for recordings that may violate typical constraints in multiview reconstruction. For example, our method is robust against inaccurate camera calibration, asynchronous capture, and imprecise depth reconstruction. Rendering results for different scenes and applications demonstrate the versatility and robustness of our approach. [ABSTRACT FROM PUBLISHER]

This paper presents a field-programmable gate array (FPGA) real-time multicamera cylindrical panorama video system. In this system, a novel simultaneous image transformation method has been studied (Chinese pattern pending No. 201310059020.1). Based on this method, perspective transformation, barrel correction, and cylindrical projection have been done at the same time for a multicamera system, resulting in an improvement of the overall performance. Using a low-cost parallel FPGA architecture, a high-speed real-time panoramic video system with low latency and high-performance has been developed. This video system can generate panoramic video of 5 \(\times \) 1280(H) \(\times \) 720(V)@15 frames/s or 5 \(\times \) 1280(H) \(\times \) 360(V)@30 frames/s in real-time on Xilinx Spartan6-150T FPGA. In order to display the panoramic video, a hardware display structure that can output the panoramic video in slide mode has also been constructed. [ABSTRACT FROM PUBLISHER]

Detecting and matching image features is a fundamental task in video analytics and computer vision systems. It establishes the correspondences between two images taken at different time instants or from different viewpoints. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a new FPGA-based embedded system architecture for feature detection and matching. It consists of scale-invariant feature transform (SIFT) feature detection, as well as binary robust independent elementary features (BRIEF) feature description and matching. It is able to establish accurate correspondences between consecutive frames for 720-p (1280x720) video. It optimizes the FPGA architecture for the SIFT feature detection to reduce the utilization of FPGA resources. Moreover, it implements the BRIEF feature description and matching on FPGA. Due to these contributions, the proposed system achieves feature detection and matching at 60 frame/s for 720-p video. Its processing speed can meet and even exceed the demand of most real-life real-time video analytics applications. Extensive experiments have demonstrated its efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

Saliency detection is widely used to extract regions of interest in images for various image processing applications. Recently, many saliency detection models have been proposed for video in uncompressed (pixel) domain. However, video over Internet is always stored in compressed domains, such as MPEG2, H.264, and MPEG4 Visual. In this paper, we propose a novel video saliency detection model based on feature contrast in compressed domain. Four types of features including luminance, color, texture, and motion are extracted from the discrete cosine transform coefficients and motion vectors in video bitstream. The static saliency map of unpredicted frames (I frames) is calculated on the basis of luminance, color, and texture features, while the motion saliency map of predicted frames (P and B frames) is computed by motion feature. A new fusion method is designed to combine the static saliency and motion saliency maps to get the final saliency map for each video frame. Due to the directly derived features in compressed domain, the proposed model can predict the salient regions efficiently for video frames. Experimental results on a public database show superior performance of the proposed video saliency detection model in compressed domain. [ABSTRACT FROM AUTHOR]

This paper presents a novel hierarchical rate control (HRC) for the Multiview Video Coding standard targeting improved bandwidth usage and high video quality. The HRC is designed to jointly address the rate control at both frame level and basic unit (BU) level. The proposed scheme is able to exploit the bitrate distribution correlation with neighboring frames to efficiently predict the future bitrate behavior by employing a model predictive control that defines a proper control action through quantization parameter (QP) adaptation. To provide a fine-grained tuning, the QP is further adapted within each frame by a Markov decision process implemented at BU level able to take into consideration a map of the regions of interest. A coupled frame/BU level feedback is performed in order to guarantee the system consistency. Experimental results show the superiority of our HRC compared to state-of-the-art solutions in terms of bitrate allocation accuracy and rate distortion while delivering smooth video quality at frame and BU levels. [ABSTRACT FROM AUTHOR]

Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning, and 3-D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in video-based behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues. [ABSTRACT FROM PUBLISHER]

Background subtraction is a fundamental preprocessing step in many surveillance video analysis tasks. In spite of significant efforts, however, background subtraction in crowded scenes remains challenging, especially, when a large number of foreground objects move slowly or just keep still. To address the problem, this paper proposes a selective eigenbackground method for background modeling and subtraction in crowded scenes. The contributions of our method are three-fold: First, instead of training eigenbackgrounds using the original video frames that may contain more or less foregrounds, a virtual frame construction algorithm is utilized to assemble clean background pixels from different original frames so as to construct some virtual frames as the training and update samples. This can significantly improve the purity of the trained eigenbackgrounds. Second, for a crowded scene with diversified environmental conditions (e.g., illuminations), it is difficult to use only one eigenbackground model to deal with all these variations, even using some online update strategies. Thus given several models trained offline, we utilize peak signal-to-noise ratio to adaptively choose the optimal one to initialize the online eigenbackground model. Third, to tackle the problem that not all pixels can obtain the optimal results when the reconstruction is performed at once for the whole frame, our method selects the best eigenbackground for each pixel to obtain an improved quality of the reconstructed background image. Extensive experiments on the TRECVID-SED dataset and the Road video dataset show that our method outperforms several state-of-the-art methods remarkably. [ABSTRACT FROM PUBLISHER]

Computer screens contain discontinuous-tone content and continuous-tone content. Thus, the most effective way for screen content coding (SCC) is to use two essentially different coders: a dictionary-entropy coder and a traditional hybrid coder. Although screen content is originally in a full-chroma (e.g., YUV444) format, the current method of compression is to first subsample chroma of pictures and then compress pictures using a chroma-subsampled (e.g., YUV420) coder. Using two chroma-subsampled coders cannot achieve high-quality SCC, but using two full-chroma coders is overkill and inefficient for SCC. To solve the dilemma, this paper proposes a mixed chroma sampling-rate approach for SCC. An original full-chroma input macroblock (coding unit) or its prediction residual is chroma-subsampled. One full-chroma base coder and one chroma-subsampled base coder are used simultaneously to code the original and the chroma-subsampled macroblock, respectively. The coder minimizing rate-distortion (R-D) is selected as the final coder for the macroblock. The two base coders are coherently unified and optimized to get the best overall coding performance and share coding components and resources as much as possible. The approach achieves very high visual quality with minimal computing complexity increment for SCC, and has better R-D performance than two full-chroma coders approach, especially in low bitrate. [ABSTRACT FROM PUBLISHER]

The lossless coding mode of the High Efficiency Video Coding (HEVC) main profile that bypasses transform, quantization, and in-loop filters is described. Compared to the HEVC nonlossless coding mode with the smallest quantization parameter value (i.e., 0 for 8-b video and -12 for 10-b video), the HEVC lossless coding mode provides perfect fidelity and an average bit-rate reduction of 3.2%–13.2%. It also significantly outperforms the existing lossless compression solutions, such as JPEG2000 and JPEG-LS for images as well as 7-Zip and WinRAR for data archiving. To further improve the coding efficiency of the HEVC lossless mode, a sample-based angular intra prediction (SAP) method is presented. The SAP employs the same prediction mode signaling method and the sample interpolation method as the HEVC block-based angular prediction, but uses adjacent neighbors for better intra prediction accuracy and performs prediction sample by sample. The experimental results reveal that the SAP provides an additional bit-rate reduction of 1.8%–11.8% on top of the HEVC lossless coding mode. [ABSTRACT FROM PUBLISHER]

A concept of a quadtree based nonsquare block coding structure is presented in this paper for the emerging High Efficiency Video Coding standard, which includes a quadtree based asymmetric motion partitioning scheme and a nonsquare quadtree transform (NSQT) algorithm. Nonsquare motion partitioning in inter frame coding provides the possibility of getting more accurate prediction results by splitting one coding block into two nonsquare prediction blocks. Contrary to the traditional symmetric motion partitions (SMP), asymmetric motion partitions (AMP) are proposed to improve the coding efficiency, especially for the coding blocks with irregular object boundaries. NSQT is designed for nonsquare prediction blocks (SMP and AMP), which combines square and nonsquare transform blocks in a unified transform structure. It exploits the directional characteristic of an image block to improve the transform efficiency. The combination of nonsquare partitions and NSQT provides high coding flexibility and low implementation cost for both encoder and decoder design. Simulation results show that about 0.9%–2.8% bit-rate saving can be achieved in terms of different configurations, and subjective quality can also be improved. [ABSTRACT FROM PUBLISHER]

Motion compensation with multiple reference pictures has been widely used during the development of the emerging High Efficiency Video Coding (HEVC) standard, which greatly helps to improve the coding efficiency. Usually, a heuristic strategy is exploited to use the nearest reconstructed pictures as references. However, such a strategy may not be efficient on all occasions, especially when different content characteristics and coding settings are considered. In this paper, we investigate how to manage reference pictures so as to achieve better rate-distortion performance under the memory constraint of the decoded picture buffer at the decoder. We formulate the reference picture management as an optimization problem and approximate its optimal solution. Moreover, we explore how to adjust quality for each picture according to the reference structure to further improve coding efficiency. For some coding cases, where a complicated encoder optimization is unaffordable, we also develop fast algorithms to get the most benefit from reference picture selection. Among them, one strategy has been adopted by the HEVC software and common test conditions to generate the anchor. Experimental results show that the proposed full search algorithm and fast search algorithms achieve significant bitrate reduction. [ABSTRACT FROM PUBLISHER]

This paper describes transform coefficient coding in the draft international standard of High Efficiency Video Coding (HEVC) specification and the driving motivations behind its design. Transform coefficient coding in HEVC encompasses the scanning patterns and coding methods for the last significant coefficient, significance map, coefficient levels, and sign data. Special attention is paid to the new methods of last significant coefficient coding, multilevel significance maps, high-throughput binarization, and sign data hiding. Experimental results are provided to evaluate the performance of transform coefficient coding in HEVC. [ABSTRACT FROM PUBLISHER]

The increasing proportion of video traffic in telecommunication networks puts an emphasis on efficient video compression technology. High Efficiency Video Coding (HEVC) is the forthcoming video coding standard that provides substantial bit rate reductions compared to its predecessors. In the HEVC standardization process, technologies such as picture partitioning, reference picture management, and parameter sets are categorized as “high-level syntax.” The design of the high-level syntax impacts the interface to systems and error resilience, and provides new functionalities. This paper presents an overview of the HEVC high-level syntax, including network abstraction layer unit headers, parameter sets, picture partitioning schemes, reference picture management, and supplemental enhancement information messages. [ABSTRACT FROM PUBLISHER]

Spatially scalable video coding (SSVC) provides an efficient way to transmit one video at different resolutions. Based on the emerging High Efficiency Video Coding (HEVC), we propose an SSVC scheme to support both single-loop (SL) and multiloop (ML) solutions by enabling different interlayer prediction mechanisms. Specifically, we employ two interlayer prediction modes: quadtree-based prediction mode (Q-mode) and learning-based prediction mode (L-mode). The Q-mode is investigated to exploit the interlayer redundancy based on the quadtree coding structure of HEVC. Due to the high correlation between layers, Q-mode utilizes the coded information from the base layer quadtree, including coding unit split, prediction unit partition, motion information, and partial texture information of transform unit, to predict the enhancement layer quadtree. By enabling Q-mode, we provide a basic SL solution for low complexity applications. Besides the correlation explored in Q-mode, we employ an extra L-mode to further improve the coding performance. In L-mode, the temporal-spatial correlation is exploited simultaneously by visual patch-based learning and mapping at pixel level. This helps us achieve more accurate prediction signals based on the coarse base layer reconstruction within an ML structure. Experimental results show the effectiveness of our SSVC scheme compared with the simulcast case and other HEVC-based SSVC schemes. [ABSTRACT FROM PUBLISHER]

This paper provides a technical overview of a newly added in-loop filtering technique, sample adaptive offset (SAO), in High Efficiency Video Coding (HEVC). The key idea of SAO is to reduce sample distortion by first classifying reconstructed samples into different categories, obtaining an offset for each category, and then adding the offset to each sample of the category. The offset of each category is properly calculated at the encoder and explicitly signaled to the decoder for reducing sample distortion effectively, while the classification of each sample is performed at both the encoder and the decoder for saving side information significantly. To achieve low latency of only one coding tree unit (CTU), a CTU-based syntax design is specified to adapt SAO parameters for each CTU. A CTU-based optimization algorithm can be used to derive SAO parameters of each CTU, and the SAO parameters of the CTU are inter leaved into the slice data. It is reported that SAO achieves on average 3.5% BD-rate reduction and up to 23.5% BD-rate reduction with less than 1% encoding time increase and about 2.5% decoding time increase under common test conditions of HEVC reference software version 8.0. [ABSTRACT FROM PUBLISHER]

We propose a content-based 3-D mosaic (CB3M) representation for long video sequences of 3-D and dynamic urban scenes captured by a camera on a mobile platform. In the first phase, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3-D and dynamic aspects of the scene under the camera coverage. In the second phase, a segmentation-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3-D objects in urban scenes, where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3-D reconstruction, and robust moving target detection. CB3M is a highly compressed visual representation for a dynamic 3-D scene, and has object contents of both 3-D and motion information. Experimental results are given for various real video sequences of large-scale 3-D scenes. [ABSTRACT FROM PUBLISHER]

Motion compensated prediction plays a vital role in achieving enormous video compression efficiency in advanced video coding standards. Most practical motion compensated prediction techniques implicitly assume pure translational motions in the video contents for effective operation. Some attempts aiming at more general motion models are usually too complex requiring parameter estimation in practical implementation. In this paper, zoom motion compensation is investigated to extend the assumed model to support both zoom and translation motions. To accomplish practical complexity, a novel and efficient subsampled block-matching zoom motion estimation technique is proposed which makes use of the interpolated reference frames for subpixel motion estimation in a conventional hybrid video coding structure. Specially designed subsampling patterns in block matching are used to realize the translation and zoom motion estimation and compensation. No zoom parameters estimation and additional frame buffers are required in the encoder implementation. The complexity of the decoder is similar to the conventional hybrid video codec that supports subpixel motion compensation. The overall increase in memory requirement and computational complexity is moderate. Experimental results show that the new technique can achieve up to 9.89% bitrate reduction using KTA2.2r1 reference software implementation. [ABSTRACT FROM PUBLISHER]

In dual frame motion compensation (DFMC), one short-term reference frame and one long-term reference frame (LTR) are utilized for motion compensation. The performance of DFMC is heavily influenced by the jump updating parameter and bit allocation for the reference frames. In this paper, first the rate-distortion performance analysis of motion compensated prediction in DFMC is presented. Based on this analysis, an adaptive jump updating DFMC (JU-DFMC) with optimal LTR selection and bit allocation is proposed. Subsequently, an error resilient JU-DFMC is further presented based on the error propagation analysis of the proposed adaptive JU-DFMC. The experimental results show that the proposed adaptive JU-DFMC achieves better performance over the existing JU-DFMC schemes and the normal DFMC scheme, in which the temporally most recently decoded two frames are used as the references. The performance of the adaptive JU-DFMC is significantly improved for video transmission over noisy channels when the specified error resilience functionality is introduced. [ABSTRACT FROM AUTHOR]

This paper presents a method to reduce the computation and memory access for variable block size motion estimation (ME) using pixel truncation. Previous work has focused on implementing pixel truncation using a fixed-block- size (16 x 16 pixels) ME. However, pixel truncation fails to give satisfactory results for smaller block partitions. In this paper, we analyze the effect of truncating pixels for smaller block partitions and propose a method to improve the frame prediction. Our method is able to reduce the total computation and memory access compared to conventional full-search method without significantly degrading picture quality. With unique data arrangement, the proposed architectures are able to save up to 53% energy compared to the conventional full-search architecture. This makes such architectures attractive for H.264 application in future mobile devices. [ABSTRACT FROM AUTHOR]

Block matching based motion estimation employs conventionally pixel-based sum of absolute differences (SAD) metric for distortion measure. Many fast matching mechanisms have been developed to lower the computational load for block matching, such as those exploiting block feature based or attribute-based SAD calculations. By examining a subblock-sum-based SAD measure, where the subblock-sums can be considered to be intra-subblock features, we propose both intra- and inter-subblock features based SAD measure for more effective block matching in terms of achieving better coding efficiency. Interestingly, the proposed feature based SAD measure can be interpreted as subblock-based sum of absolute Hadamard transformed differences (SATD). The new features can be constructed in a multilevel structure, which may provide a flexible and scalable means for a tradeoff between computation load and coding efficiency. Encoding results are shown to compare the proposed scheme against other relevant feature based SAD measures. [ABSTRACT FROM AUTHOR]

Integrating video coding and denoising is a novel processing paradigm, bringing mutual benefits to both video processing tools. In this paper, we propose a novel video denoising approach of which the main idea is reusing motion estimation resources from the video coding module for video denoising. In most cases, the motion fields produced by real-time video codecs cannot be directly employed in video denoising, since they, as opposed to noise filters, tolerate errors in the motion field. In order to solve this problem, we propose a novel motion-field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. Additionally, a novel temporal filter is proposed that is robust against errors in the estimated motion field. Numerical results demonstrate that the proposed denoising scheme is of low-complexity and compares favorably to the state-of-the-art video denoising methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel B-picture coding based on block-wise adaptive motion accuracy (BAMA) with low-complexity motion compensation (MC). BAMA is able to adaptively select the motion accuracy for each inter-block in a B-picture depending on whether it is of bidirectional prediction, in which compared to the unidirectionally predicted block, lower motion accuracy is applied to the bidirectionally predicted block to reduce MC complexity. To further reduce MC complexity for the bidirectionally predicted luminance block with subpixel motion vectors in both directions, the forward and backward MC interpolations are merged according to the linear operation property of MC interpolation. In addition, a simplified MC interpolation method is also introduced for chrominance components for the bidirectionally predicted block. When integrating the proposed methods into H.264/AVC reference software, the experimental results demonstrate that they can significantly reduce MC complexity for a B-picture while yielding the comparable rate distortion performance in comparison with H.264/AVC although they no longer generate H.264/AVC compliant bitstream. [ABSTRACT FROM AUTHOR]

Effectively coding multiview visual content is an indispensable research topic because multiview image and video that provide greatly enhanced viewing experiences often contain huge amounts of data. Generally, conventional hybrid predictive-coding methodologies are adopted to address the compression by exploiting the temporal and interviewpoint redundancy existing in a multiview image or video sequences. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in interviewpoint prediction or disparity estimation (DE), because interviewpoint disparity is completely different from temporal motion existing in the conventional video. Targeting a generic fast DE framework for interviewpoint prediction, we propose a novel DE technique in this paper to accelerate the disparity search by employing epipolar geometry. Theoretical analysis, optimal disparity vector distribution histograms, and experimental results show that the proposed epipolar geometry-based DE can greatly reduce search region and effectively track large and irregular disparity, which is typical in convergent multiview camera setups. Compared with the existing state-of-the-art fast ME approaches, our proposed DE can obtain a similar coding efficiency while achieving a significant speedup for interviewpoint prediction and coding. Moreover, a robustness study shows that the proposed DE algorithm is insensitive to the epipolar geometry estimation noise. Hence, its wide application for multiview image and video coding is promising. [ABSTRACT FROM AUTHOR]

In this paper, we address the key problem in real-time video coding, the rate-distortion (R-D) tradeoff. As most video coding applications employ color images, we analyze the R-D modeling problem of the color image sequence. We investigate that the R-D characteristics of color video signal in traditional transform-based video coding systems should be modeled for the luminance and chrominance components separately. So we propose separable R-D models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach to avoid buffer overflow and underflow. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel R-D tradeoff controller is designed. In the proposed R-D tradeoff framework, both the quality variation and buffer safety are considered. Extensive simulation results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow. [ABSTRACT FROM AUTHOR]

Subpixel motion estimation plays an important role in compression efficiency within modern video codecs such as MPEG2, MPEG4, and H.264. Subpixel motion estimation is implemented within these standards using interpolated values at 1/2 or 1/4 pixel accuracy. Such interpolation gives a good reduction in residual energy for each predicted macroblock and, therefore, improves compression. However, this leads to a significant increase in computational complexity at the encoder. This is especially true for H.264 where the cost of an exhaustive set of macroblock segmentations need to be estimated in order to obtain an optimal mode for prediction. This paper presents a novel interpolation-free scheme for subpixel motion estimation using the result of the full pixel sum of absolute difference distribution of each motion compensated block applied to an H.264 encoder. This system produces reduced complexity motion estimation with a controllable tradeoff between compression performance and encoder speed. These methods facilitate the generation of a real time software H.264 encoder. [ABSTRACT FROM AUTHOR]

H.264/AVC encoder employs a complex mode-decision technique based on rate-distortion optimization. It calculates rate-distortion cost (RDcost) for all possible modes to choose the best one having the minimum RDcost. This paper presents a frame-layer rate control for H.264/AVC that computes the Lagrange multiplier (λM0DE) for mode decision by using a quantization parameter (QP) which may be different from that used for encoding. At the same time, we also compare actual bits produced by previous macroblocks (MBs) with the total bits allocated to these MBs to further modify λMODE. The objective of these measures aims to produce bits as close to the frame target bits for rate control as pos- sible. This is very important in the case of low-bit-rate tight buffer applications. In order to obtain an accurate QP for a frame, we employ a complexity-based bit-allocation scheme and a QP adjust- ment method. Simulation results comparing with the 11.264 Joint Video Team (JVT) rate control method show that the H.264 en- coder, using the proposed algorithm, achieves a visual quality im- provement of about 0.56 dB, performs better for buffer overflow and underfiow, and achieves a smaller PSNR deviation. [ABSTRACT FROM AUTHOR]

In this paper, a novel architecture for two-dimensional (2-D) inverse discrete cosine transform (IDCT) is implemented using 90-nm CMOS technology. By exploiting the sparseness property of a 2-D discrete cosine transform (DCT) coefficient matrix and the even and odd symmetry properties of the basis vectors of the one-dimensional (1-D) DCT, the proposed architecture reduces computational complexity and increases a throughput rate effectively. First, we derive a recursion equation from the definition of the 2-D IDCT algorithm and use it to design an efficient 2-D IDCT architecture. The proposed architecture consisting of processing elements is suitable for very-large-scale-integration implementation due to its highly regular and scalable structure for 2-D N x N IDCT computations. Based on the derived recursion equation, we show how data flow for the outer products scaled by only nonzero 2-D DCT coefficients performs 2-D IDCT naturally with low complexities of the controller and the interconnection. The proposed architecture based on the recursion equation provides optimum balance in both hardware complexity and throughput rate when compared with other 2-D IDCT architectures. [ABSTRACT FROM AUTHOR]