Distributed Video Coding Research Articles

A Symbol Based Distributed Video Coding System Using Multiple Hypotheses

A Symbol Based Distributed Video Coding System Using Multiple Hypotheses

Motion-Aware Mesh-Structured Trellis for Correlation Modelling Aided Distributed Multi-View Video Coding

A joint source-channel coding has attracted substantial attention with the aim of further exploiting the residual correlation residing in the encoded video signals for the sake of improving the reconstructed video quality. In our previous paper, a first-order Markov process model was utilized as an error concealment tool for exploiting the intra-frame correlation residing in the Wyner-Ziv (WZ) frame in the context of pixel-domain distributed video coding. In this contribution, we exploit the interview correlation with the aid of an interview motion search in distributed multi-view video coding (DMVC). Initially, we rely on the system architecture of WZ coding invoked for multiview video. Then, we construct a novel mesh-structured pixel-correlation model from the inter-view motion vectors and derive its decoding rules for joint source-channel decoding. Finally, we benchmark the attainable system performance against the existing pixel-domain WZ coding based DMVC scheme, where the classic turbo codec is employed. Our simulation results show that substantial bitrate reductions are achieved by employing the proposed motion-aware mesh-structured correlation modelling technique in a DMVC scheme.

Low complexity distributed video coding

ContextConventional video encoding is a computationally intensive process that requires a lot of computing resources, power and memory. Such codecs cannot be deployed in remote sensors that are constrained in terms of power, memory and computational capabilities. For such applications, distributed video coding might hold the answer. ObjectiveIn this paper, we propose a distributed video coding (DVC) architecture that adheres to the principles of DVC by shifting the computational complexity from the encoder to the decoder and caters to low-motion scenarios like video conferencing and surveillance of hallways and buildings. MethodThe architecture presented is block-based and introduces a simple yet effective classification scheme that aims at maximizing the use of skip blocks to exploit temporal correlation between consecutive frames. In addition to the skip blocks, a dynamic GOP size control algorithm is proposed that instantaneously alters the GOP size in response to the video statistics without causing any latency and without the need to buffer additional frames at the encoder. To facilitate real-time video delivery and consumption, iterative channel codes like low density parity check codes and turbo codes are not used and in their place a Bose–Chaudhuri–Hocquenghem (BCH) code with encoder rate control is used. ResultsIn spite of reducing the complexity and eliminating the feedback channel, the proposed architecture can match and even surpass the performance of current DVC systems making it a viable solution as a codec for low-motion scenarios. ConclusionWe conclude that the proposed architecture is a suitable solution for applications that require real-time, low bit rate video transmission but have constrained resources and cannot support the complex conventional video encoding solutions. Practical implicationsThe practical implications of the proposed DVC architecture include deployment in remote video sensors like hallway and building surveillance, video conferencing, video sensors that are deployed in remote regions (wildlife surveillance applications), and capsule endoscopy.

Suppressing feedback in a distributed video coding system by employing real field codes

Single-view distributed video coding (DVC) is a video compression method that allows for the computational complexity of the system to be shifted from the encoder to the decoder. The reduced encoding complexity makes DVC attractive for use in systems where processing power or energy use at the encoder is constrained, for example, in wireless devices and surveillance systems. One of the biggest challenges in implementing DVC systems is that the required rate must be known at the encoder. The conventional approach is to use a feedback channel from the decoder to control the rate. Feedback channels introduce their own difficulties such as increased latency and buffering requirements, which makes the resultant system unsuitable for some applications. Alternative approaches, which do not employ feedback, suffer from either increased encoder complexity due to performing motion estimation at the encoder, or an inaccurate rate estimate. Inaccurate rate estimates can result in a reduced average rate-distortion performance, as well as unpleasant visual artifacts. In this paper, the authors propose a single-view DVC system that does not require a feedback channel. The consequences of inaccuracies in the rate estimate are addressed by using codes defined over the real field and a decoder employing successive refinement. The result is a codec with performance that is comparable to that of a feedback-based system at low rates without the use of motion estimation at the encoder or a feedback path. The disadvantage of the approach is a reduction in average rate-distortion performance in the high-rate regime for sequences with significant motion.

Distributed video coding with block mode decision to reduce temporal flickering

The common distributed video coding (DVC) systems treat input video frames group by group. In each group of pictures (GOP), usually the first frame, called key frame, is intra-coded and the others are Wyner-Ziv (WZ)-coded. This GOP coding structure presents a fixed and inefficient coding mode switch (key or WZ) at group boundaries, thus preventing a DVC system from adapting the rate-distortion (R-D) performance to varying video content. In this work, a structure of temporal group of blocks (TGOB) with dynamic coding mode (key/WZ) decision is presented. This dynamic TGOB coding architecture determines each image block to be a key block or a WZ block based on spatiotemporal image analysis, resulting in a mode switch of fine granularity in both the spatial and temporal domains. As a consequence, not only the overall coding efficiency is improved, but also the temporal flickering artifacts for the reconstructed video are reduced substantially. Experimental results show that our proposed DVC scheme with block mode decision achieves up to 2.85 dB of quality gain and also up to 51% of temporal flickering reduction, compared to the well-known DISCOVER system.

Perspective transform motion modeling for improved side information creation

The distributed video coding (DVC) paradigm is based on two well-known information theory results: the Slepian-Wolf and Wyner-Ziv theorems. In a DVC codec, the video signal correlation is mostly exploited at the decoder, providing a flexible distribution of the computational complexity between the encoder and the decoder and error robustness to channel errors. To exploit the temporal correlation, an estimate of the original frame to code, well-known as side information, is typically created at the decoder. One popular approach to side information creation is to perform frame interpolation using a translational motion model derived from already decoded frames. However, this translational model fails to estimate complex camera motions, such as zooms and rotations, and is not accurate enough to estimate the true trajectories of scene objects. In this paper, a new side information creation framework integrating perspective transform motion modeling is proposed. This solution is able to better locally track the trajectories and deformations of each object and increase the accuracy of the overall side information estimation process. Experimental results show peak signal-to-noise ratio gains of up to 1 dB in side information quality and up to 0.5 dB in rate-distortion performance for some video sequences regarding state-of-the-art alternative solutions.

Evaluation of Side Information Effectiveness in Distributed Video Coding

The rate-distortion performance of a distributed video coding system strongly depends on the characteristics of the side information. One could naïvely think that the best side information is the one with the largest PSNR with respect to the original corresponding image. However, previous works have shown that this is not always the case and a reduction of the side information MSE does not always translate into better rate-distortion performance for the complete system. The scope of this paper is to explore a set of metrics other than the PSNR and explicitly designed to classify the side information with respect to its impact on the end-to-end compression performance. A first contribution is to define an experimental framework that can be used to meaningfully compare different metrics for side information evaluation. As a second contribution, our analysis allows to understand why in some cases PSNR-based metrics provide a fairly reliable estimation of the side information quality, while in other cases they do not. This analysis also allows us to introduce a set of new metrics that are better adapted for side information effectiveness evaluation, and that are based on a suitable power of the absolute difference between side information and the original image, or on the Hamming distance between the respective transform coefficients. Besides their theoretical interest, these new metrics can also improve the rate-distortion performance of some distributed video coding systems such as the hash-based ones. We observe improvement up to 74% rate reduction in a simple study case.

Spatial correlation-based side information refinement for distributed video coding

Distributed video coding (DVC) architecture designs, based on distributed source coding principles, have benefitted from significant progresses lately, notably in terms of achievable rate-distortion performances. However, a significant performance gap still remains when compared to prediction-based video coding schemes such as H.264/AVC. This is mainly due to the non-ideal exploitation of the video sequence temporal correlation properties during the generation of side information (SI). In fact, the decoder side motion estimation provides only an approximation of the true motion. In this paper, a progressive DVC architecture is proposed, which exploits the spatial correlation of the video frames to improve the motion-compensated temporal interpolation (MCTI). Specifically, Wyner-Ziv (WZ) frames are divided into several spatially correlated groups that are then sent progressively to the receiver. SI refinement (SIR) is performed as long as these groups are being decoded, thus providing more accurate SI for the next groups. It is shown that the proposed progressive SIR method leads to significant improvements over the Discover DVC codec as well as other SIR schemes recently introduced in the literature.

A New Scheme Based on Pixel-Intense Motion Block Algorithm for Residual Distributed Video Coding

Residual Distributed Video Coding (RDVC) is a branch of the Distributed Video Coding (DVC). By reducing the coding rate, the RDVC scheme gets a better rate-distortion performance. The overall performance of the traditional pixel-based RDVC scheme is better than the traditional transform-based domain DVC scheme. But the traditional RDVC scheme also has some application limitations; for example, the performance of RDVC is bad when it is applied to intense motion regions. And the pixel-based RDVC scheme cannot fully exploit the spatial correlations of the original information. In this paper, we propose a new approach of residual coding combined IMB (intense motion block) extraction algorithm with Low Density Parity Check (LDPC) and Baum-Welch iterative decoding algorithm. Experimental results show that the proposed scheme achieves better rate-distortion performance compared to traditional DVC and RDVC scheme.

Distributed adaptive compressed video sensing using smoothed projected landweber reconstruction

A novel Compressed-Sensing-based (CS-based) Distributed Video Coding (DVC) system, called Distributed Adaptive Compressed Video Sensing (DISACOS), is proposed in this paper. In this system, the input frames are divided into key frames and non-key frames, which are encoded by block CS sampling. The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber (SPL) algorithm using multi-hypothesis predictions. For the non-key frames, a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information (SI) generated by the conventional interpolation or extrapolation at the decoder; then, another group of CS measurements are sampled again upon the decoder's request. To fully utilise the CS measurements, we adaptively allocate these measurements to each block in terms of different edge features. Finally, the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI. Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.

Encoder-driven rate control and mode decision for distributed video coding

To provide low-complexity encoding for video in unidirectional or offline compression scenarios, this paper proposes an efficient feedback-channel-free distributed video coding architecture featuring a novel encoder-driven rate control scheme in tandem with a designated mode selection process. To this end, the encoder features a novel low-complexity motion estimation technique to approximate the side-information (SI) available at the decoder. Then, a SI-dependent correlation channel estimation between the approximated SI and the original frames is used to derive the theoretically required rate for successful Slepian-Wolf (SW) decoding. Based on the evaluation of the expected trade-off between the estimated required coding rate and the estimated distortion outcome, a novel encoder-side mode decision module assigns a different coding mode to distinct portions of the coded frames. In this context, skip, intra and SW coding modes are supported. To reduce the effect of underestimation, the final SW rate is adjusted upwards using a novel rate formula. Additionally, a successive SI refinement technique is exploited at the decoder to decrease the number of SW decoding failures. Experimental results illustrate the benefit of the different coding options and show similar or superior compression performance with respect to the feedback-based DISCOVER benchmark system. Finally, the low-complexity encoding characteristics of the proposed system are confirmed, as well as the beneficial impact of the proposed scheme on the decoding complexity.

Novel solutions for side information generation and fusion in multiview DVC

Abstract One of the key problems in distributed video coding is the generation of side information. This task consists of producing an estimate of an image with some neighboring ones, such as those taken by the same camera at different time instants, or, in the case of multiview setups, images taken at the same time instant by different cameras. If both estimates are available, a further problem arises, which is how to merge them in order to create a single side information. This problem is very relevant since a good estimate of the unknown image will require only a few bits to be corrected. Considering a multiview distributed video-coding setup, we propose a novel technique for inter-view interpolation based on occlusion prediction, a new fusion technique from multiple estimates, and finally an adaptive validation step for switching among the three possible side information images: temporal, inter-view, and fusion. We provide a comprehensive set of experimental results, which indicate bit rate reductions of more than 9% in average; moreover, we observe much more consistent results with respect to state-of-the-art techniques.

Side Information Refinement for Transform domain Distributed Video Coding

A new side information refinement method for transform domain distributed video coding (DVC) is proposed. In DVC, side information is the frame constructed from key frames which are intra coded. It is utilized as the initial estimate of source symbols of Wyner-Ziv coded frame which is not transmitted to the decoder. Errors in the estimated frames are to be corrected by the decoder using the parity bits which are the only information received from the encoder. Quality of decoder generated side information is one of the most important factors deciding the overall performance of the decoder. A new refinement method of improving the quality of side information of the DVC decoder is proposed. The side information frame is refined at the end of each transform band decoding stage followed by a conventional bi-directional motion estimation and compensation technique. Proposed side information refinement technique consequently improves the quality of final reconstructed pictures.

Perceptually driven video error protection using a distributed source coding approach

In video communication systems, the video signals are typically compressed and sent to the decoder through an error-prone transmission channel that may corrupt the compressed signal, causing the degradation of the final decoded video quality. In this context, it is possible to enhance the error resilience of typical predictive video coding schemes using as inspiration principles and tools from an alternative video coding approach, the so-called Distributed Video Coding (DVC), based on the Distributed Source Coding (DSC) theory. Further improvements in the decoded video quality after error-prone transmission may also be obtained by considering the perceptual relevance of the video content, as distortions occurring in different regions of a picture have a different impact on the user's final experience. In this context, this paper proposes a Perceptually Driven Error Protection (PDEP) video coding solution that enhances the error resilience of a state-of-the-art H.264/AVC predictive video codec using DSC principles and perceptual considerations. To increase the H.264/AVC error resilience performance, the main technical novelties brought by the proposed video coding solution are: (i) design of an improved compressed domain perceptual classification mechanism; (ii) design of an improved transcoding tool for the DSC-based protection mechanism; and (iii) integration of a perceptual classification mechanism in an H.264/AVC compliant codec with a DSC-based error protection mechanism. The performance results obtained show that the proposed PDEP video codec provides a better performing alternative to traditional error protection video coding schemes, notably Forward Error Correction (FEC)-based schemes.

An improved side information generation for distributed video coding

This paper presents a side information (SI) scheme for distributed video coding based on multilayer perceptron. The suggested scheme predicts a Wyner–Ziv (WZ) frame from two decoded key frames. The network is trained offline using patterns from different standard video sequences with varied motion characteristics to achieve generalization. The proposed scheme is simulated along with other standard video coding schemes. Performance comparisons have been made with respect to training convergence, rate distortion (RD), peak signal to noise ratio (PSNR), number of requests per SI frame, decoding time requirement, etc. In general, it is observed that the proposed scheme has a superior SI frame generation capability as compared to its competent schemes.

Low‐complexity distributed multiple description coding for wireless video sensor networks

Quality-of-service-guaranteed video communication in wireless video sensor networks (WVSNs) is extremely challenging because of the unique constraints of WVSN (e.g. limited resources of sensors and high error rates in wireless sensor networks) and the characteristics of video traffic (e.g. huge data rate and tight delay bounds). Distributed video coding (DVC) has emerged as a new video coding paradigm for video applications with resource-limited encoders. However, current DVC architectures still have several technical limitations that prevent their widespread use in real-world applications. A low-complexity distributed multiple description coding method that combines the principles of multiple description coding and DVC has been proposed to further improve the error resilience of DVC, while maintaining low encoder complexity and good rate-distortion performance in WVSN. Simulation results show that the proposed method is robust against transmission errors, while maintaining low encoder complexity and low system latency for resource-limited applications in WVSNs.

A Coding and Postprocessing Framework of Multiview Distributed Video for Wireless Video Sensor Networks

In many surveillance application scenarios of wireless video sensor networks (WVSNs), a number of video sensors are deployed, and multidimension monitored the visual information in a region of interest, forming multiview videos. Since the power, computing capability, and bandwidth are very limited in WVSNs, the conventional multiview video coding method is no longer applicable. So multiview distributed video coding (MDVC) emerged and developed rapidly. In this paper, we propose a new multiview video coding and postprocessing framework for multiview videos. First, in coding scheme, motion intense regions (MIRs) and nonmotion intense regions (NMIRs) based on sum of absolute difference (SAD) criteria are distinguished. For the MIR, the side information (SI) is generated by fusion temporal SI and interview spatial SI at the pixel level. But for the NMIR, the temporal SI is directly use as the ultimate SI. Then, to further improve the quality of the decoded image, an image postprocessing scheme is designed by using deblocking and deringing artifact filters on decoded image. Finally, a set of experimental results show that the proposed fusion SI approach can bring improvements up to 0.2–0.5 dB when compares with only temporal SI used. The subsequent decoded videos postprocessing simulation proves that the proposed postprocessing scheme can provide an additional improvement of about 0.1 dB to the decoded video sequences.

Dictionary learning based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel “undersampled” correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1–3.5dB improvements in the average PSNR, and a 2–9dB gain for non-key frames when key frames are subsampled at an increased rate.

Distributed video coding of secure compressed sensing

AbstractIn this paper, we use the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, we use the quantum‐behaved particle swarm optimization algorithm to reconstruct video signal. In this paper, we analyze the computational complexity of two algorithms, simulation results demonstrate that quantum‐behaved particle swarm optimization algorithm greatly reduce the amount of computation. And it has better stability and faster convergence to deal with original video signal. Simulation results demonstrate that we can obtain the better reconstructed video with low sample value, and it can guarantee safety performance. Copyright © 2013 John Wiley & Sons, Ltd.

Multimedia Communications Using a Fast and Flexible DVC to H.264/AVC/SVC Transcoder

The evolution of network technologies and mobile devices (equipped with low-cost video cameras) offer new multimedia services for mobile telephony, such as video communications. However, this kind of multimedia services needs to meet special requirements in terms of low complexity on both sides of the communication. Currently, most of mobile video communications are based on traditional codecs, which concentrates high complexity on the encoder side. Then, Distributed Video Coding tackles the problem of tougher complexity constraints for encoding algorithms at the expense of increasing decoder complexity. Taking into account the benefits of both paradigms, Distributed Video Coding to H.264 transcoding provides such multimedia systems with low complexity on both sides. Moreover, there is a H.264 extension called Scalable Video Coding which supports a variety of networks and devices. This proposed scheme moves the highly-complex processes to the transcoder, which has more resources. However, to achieve a low-delay transmission between mobile devices, transcoder time must be reduced. For this purpose, this paper focuses on reducing the complexity of the transcoder. To start with, the first transcoding stage is improved by means of a multicore processor, which executes the decoding algorithm in parallel. Then, the second stage uses the motion vectors generated during the first decoding stage to reduce the motion estimation complexity of the H.264 encoder and of its scalable extension as well. To support different Distributed Video Coding/H.264 patterns and profiles, the proposed transcoder includes a mapping between different kinds of frames and GOP lengths from both paradigms. As a result, this paper proposes an efficient algorithm to support mobile-to-mobile video communications which reduces the transcoding time about 70 % without significant rate-distortion penalty.