Transform Domain Wyner-Ziv Video Research Articles

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.

Distributed video coding with adaptive two-step side information generation for smart and interactive media

Conventional video coding standards, such as MPEGx and H.26x, use a hybrid architecture of block-based motion compensation and discrete cosine transform (DCT) within the structure of a complex encoder and a simple decoder. Contrary to conventional video codecs, Wyner-Ziv (WZ) video coding, which is a practical application of distributed video coding (DVC) based on the Slepian-Wolf and WZ theorems, exploits the source correlation at the decoder, thereby allowing for the development of simpler encoders. However, the current WZ video coding algorithms cannot match the coding performance of conventional video coding. In order to improve the coding performance of transform-domain WZ video coding, an adaptive two-step side-information generation method is thus proposed for evaluation and analysis in this study. The proposed method uses decoded WZ frames in the down-sampled spatial resolution along with already decoded coefficients to successively improve rate-distortion (RD) performance as the decoding progresses. The experimental results show that the proposed method, compared to previous WZ video coding method, shows improved coding performance, particularly under critical conditions, such as cases with high motion content.

Perception-based adaptive quantization for transform-domain Wyner-Ziv video coding

Distributed video coding (DVC) is desirable for encoding systems with tight power or computational constraints, for which the popular practical solution is transform-domain Wyner-Ziv video coding (TD-WZVC). To achieve the similar coding performance with H.264/AVC, quantization is a key factor in TD-WZVC. Practically, the quantization matrix is trained offline and remains fixed value during coding. Optimal rate-distortion (RD) performance cannot be achieved due to the varying quality of side information (SI) frame. In this paper, a novel model of perceptual distortion probability is developed to estimate the perceptual distortion of SI frame and to derive the target perceptual distortion. With the two perceptual distortion probabilities, three components (i.e. quality of SI frame, perceptual features and RD optimization) are integrated to determine the optimal quantization matrix adaptively, which improves the coding performance. Extensive experiments demonstrate that the proposed scheme can adaptively determine proper quantization matrix online and achieve similar visual quality with less bit-rate, as compared to other adaptive quantization schemes in TD-WZVC.

Rate-Constrained Region of Interest Coding Using Adaptive Quantization in Transform Domain Wyner–Ziv Video Coding

In this paper, we propose a rate-constrained distributed video coding-based region of interest (ROI) coding scheme. The proposed scheme appropriately determines ROI according to an available bit budget that depends on transmission channel and decoding environment. Its subjective quality is faithfully maintained via adaptive ROI definition of the available resources. Prior knowledge about the ROI is represented in terms of Gaussian mixture weighting function, which in turn determines the ROI according to the number of available bits. An adaptive quantization method is used in the ROI bit allocation. Compared to the existing non-ROI-based schemes, the proposed scheme not only improves the ROI coding performance but also the subjective quality of the entire picture.

Re-estimation of Motion and Reconstruction for Distributed Video Coding

Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.

A mixed framework for transform domain Wyner–Ziv video coding

This paper presents a mixed framework based on an efficient intra key frame coding and an improved side information (SI) generation scheme in transform domain Wyner–Ziv (WZ) video coding. The performance of the WZ video coding strongly depends on the quality of the SI. The SI can be generated from the decoded key frames resulted from intra key frame video coding. The better the decoded key frames are the better would be the SI generation. In this paper, a Burrows–Wheeler transform (BWT) based intra-frame video coding is proposed to generate improved decoded key frames. Furthermore, an improved SI generation scheme with multilayer perceptron (MLP) is proposed. Comparative analysis with other standard techniques of WZ video coding reveals that the proposed scheme has better standing as compared to its counterparts in terms of both coding efficiency and improved perceptual quality.

Distributed Video Coding Based on Multiple-source Correlation Model

Distributed Video Coding (DVC) is a new video coding paradigm which has the ability to shift coding complexity from encoder to decoder. State-of-art DVC schemes are mainly based on two-source correlation model and only one correlation channel between side information and wyner-ziv frame is considered. In this paper, multiple-source correlation model is introduced into a famous DVC scheme, namely Transform Domain Wyner-Ziv video coder (TDWZ). A soft-input computing algorithm based on multiple-source correlation model is firstly proposed. The correlations a mong multiple side information are separately exploited. Then a new reconstruction function based on multiple source correlation model is also proposed to improve reconstructed image's quality. Experimental results show that the proposed algorithms can effectively improve the coding performance of systems.

Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding

Distributed video coding (DVC) reduces the complexity at the encoder by using source statistics at the decoder. However, a performance gap still exists between DVC and hybrid video coders due to the inaccuracy of correlation noise (CN) modeling and inefficient exploitation of side information. We propose improvements to the performance of DVC using the two following aspects. Firstly, a progressive refinement method is proposed to improve the accuracy of CN modeling for transform domain Wyner-Ziv video coding, in which previously decoded bitplanes are exploited to progressively refine estimated CN as bitplane decoding proceeds. Secondly, a maximum likelihood pre-decoding method is also proposed to obtain a further reduction in bitrate. In our method, bitplanes are pre-decoded first using the conditional bit probability without syndrome bits. The bitrate is reduced by avoiding requests for syndrome bits for the bitplanes having strong temporal correlation. The experimental results show that our proposed methods could provide bitrate savings up to 8.26% and PSNR gains up to 0.2 dB without a significant increase in decoding complexity.

Flexible Complexity Control Solution for Transform Domain Wyner-Ziv Video Coding

Most Wyner-Ziv (WZ) video coding solutions in the literature focus on improving the coding efficiency. Recently, a few papers have addressed problems related to the complexity distribution of WZ video coding by sharing the motion estimation process between the encoder and decoder. However, these methods turn out to significantly increase the computational complexity of the encoding process, due to the presence of motion estimation at the encoder, which is less appealing in WZ coding applications, since their primary requirement is a low-encoding complexity. To address this problem, we propose a different approach to complexity control based on the adaptive selection between WZ and intra coding in the WZ frames. This solution is more flexible than the previous ones in that it can be implemented either at the encoder or decoder. The proposed intra mode selection algorithm exploits the spatial and temporal coherency in the transform domain Wyner-Ziv video coding (TDWZ), in order to achieve complexity control between the WZ encoder and decoder. The experimental results illustrate that the proposed algorithm not only effectively distributes the computational complexity over the encoder and decoder, but also retains the low-complexity feature at the encoder. This should make it attractive for a large number of real video applications of the WZ video coding paradigm. Moreover, the coding efficiency of the conventional TDWZ codec without intra mode decision is improved by up to 2 dB by the proposed intra mode selection algorithm.

An Efficient Encoder Rate Control Solution for Transform Domain Wyner–Ziv Video Coding

Most Wyner-Ziv (WZ) video coding solutions in the literature use a feedback channel (FC) based decoder rate control (DRC) strategy to adjust the bitrate to correct the side information (SI) errors. More recently, some encoder rate control (ERC) strategies have been proposed to address application scenarios where a FC is not available. The ERC based WZ video coding RD performance depends not only on the (encoder) parity rate estimator (PRE) accuracy but also on the decoder “intelligence” in dealing with the residual errors due to parity rate underestimation. In this context, the main objective of this paper is to propose a more efficient and powerful ERC solution for transform domain WZ (TDWZ) video coding by simultaneously tackling the two issues aforementioned with the following technical novelty: 1) integration in an ERC context of Gray mapping for the quantized DCT coefficients to enhance the correlation between WZ and SI data; 2) more accurate PRE to better estimate the needed parity rate to avoid undesired parity rate underestimations and overestimations; 3) novel soft reconstruction function to reduce the impact of the residual bitplane errors in the decoded WZ frame quality; and 4) weighted overlapped block motion compensation technique to refine the SI used in an iterative WZ decoding framework with the correlation noise model parameters dynamically updated. Experimental results show a considerable RD performance improvement with a reduction of up to about 2 dB of the gap between the ERC and DRC based approaches in TDWZ video coding solutions, thus making this ERC based WZ codec the most efficient available and competitive regarding DRC based WZ video coding solutions.

Cross-band noise model refinement for transform domain Wyner–Ziv video coding

Distributed Video Coding (DVC) is a new video coding paradigm, which mainly exploits the source statistics at the decoder based on the availability of decoder side information. One approach to DVC is feedback channel based Transform Domain Wyner–Ziv (TDWZ) video coding. The efficiency of current TDWZ video coding trails that of conventional video coding solutions, mainly due to the quality of side information, inaccurate noise modeling and loss in the final coding step. The major goal of this paper is to enhance the accuracy of the noise modeling, which is one of the most important aspects influencing the coding performance of DVC. A TDWZ video decoder with a novel cross-band based adaptive noise model is proposed, and a noise residue refinement scheme is introduced to successively update the estimated noise residue for noise modeling after each bit-plane. Experimental results show that the proposed noise model and noise residue refinement scheme can improve the rate-distortion (RD) performance of TDWZ video coding significantly. The quality of the side information modeling is also evaluated by a measure of the ideal code length.

Efficient Motion Field Interpolation Method for Wyner-Ziv Video Coding

Wyner-Ziv video coding has the capability to reduce video encoding complexity by shifting motion estimation procedure from encoder to decoder. Amongst many motion estimation methods, e xpectation m aximization algorithm is the most effective one. Unfortunately, the implementation of block-based motion estimation in this algorithm causes motion field profile bounded by granularity of block size. Nearest-neighbor and bilinear interpolation methods have already applied in multiview image coding to handle similar problem. This paper aims to evaluate performance of both interpolation methods in transform-domain Wyner-Ziv video codec. Results showed that bilinear interpolation effective only for high motion video sequences. In this scenario, it has bitrate saving up to 3.29 %, 0.2 dB higher PSNR, and 12.30 % higher decoding complexity compared to nearest-neighbor. In low motion video content, bitrate saving only gained up to 0.82%, with almost the same PSNR, while decoding complexity increase up to 10.32%.

Wyner–Ziv Video Coding With Classified Correlation Noise Estimation and Key Frame Coding Mode Selection

We improve the overall rate-distortion performance of distributed video coding by efficient techniques of correlation noise estimation and key frame encoding. In existing transform-domain Wyner-Ziv video coding methods, blocks within a frame are treated uniformly to estimate the correlation noise even though the success of generating side information is different for each block. We propose a method to estimate the correlation noise by differentiating blocks within a frame based on the accuracy of the side information. Simulation results show up to 2 dB improvement over conventional methods without increasing encoder complexity. Also, in traditional Wyner-Ziv video coding, the intercorrelation of key frames is not exploited since they are simply intracoded. In this paper, we propose a frequency band coding mode selection for key frames to exploit similarities between adjacent key frames at the decoder. Simulation results show significant improvement especially for low-motion and high frame rate sequences. Furthermore, the advantage of applying both schemes in a hierarchical order is investigated. This method achieves additional improvement.

병렬 LDPCA 채널코드 부호화 방법을 사용한 고속 분산비디오부호화

In this paper, we propose a parallel LDPCA encoding method for fast transform-domain Wyner-Ziv video encoding which is suitable in an ultra fast and low power video encoding. The conventional transform-domain Wyner-Ziv video encoding performs LDPCA channel coding of quantized transform coefficients in bitplane-serial fashion, which takes about 60% of total encoding time, and this computational complexity becomes severer as the bitrate increases. The proposed method binds several bitplanes into one packed message and carries out the LDPCA encoding in parallel. The proposed LDPCA encoding method improves the encoding speed by 8 ~ 55 times. In the experiment, the proposed Wyner-Ziv encoder can encode 700 ~ 2,300 QCIF size frames per second with GOP=64. The method can be applied to the pixel-domain Wyner-Ziv encoder using LDPCA, and has a wide scope of application.

Feedback-free rate-allocation scheme for transform domain Wyner–Ziv video coding

In most existing Wyner---Ziv video coding schemes, a feedback channel (FC) is expected at the decoder in order to allocate a proper bit rate for each Wyner---Ziv frame. However, FC not only results in additional latency but also increases decoding complexity due to the several feedback-decoding iterations. Moreover, FC may be unavailable in many practical video applications. In this paper, we propose a novel feedback-free rate-allocation scheme for transform domain Wyner---Ziv video coding (TD-WZVC), which predicts the rate for each Wyner---Ziv frame at the encoder without significantly increasing the complexity of the encoder. First, a correlation estimation model is presented to characterize the relationship between the source frame and the reference frame estimated at the encoder in TD-WZVC. Then, an efficient FC-free rate-allocation algorithm is proposed and a linear model is built to avoid both overestimation and underestimation of the real rate and obtain an optimal rate-distortion performance. Experimental results show that the proposed scheme is able to achieve a good encoder rate allocation while still maintaining consistent coding efficiency.

Statistical motion learning for improved transform domain Wyner–Ziv video coding

Wyner–Ziv (WZ) video coding is a particular case of distributed video coding (DVC), the recent video coding paradigm based on the Slepian–Wolf and Wyner–Ziv theorems which exploits the source temporal correlation at the decoder and not at the encoder as in predictive video coding. Although some progress has been made in the last years, WZ video coding is still far from the compression performance of predictive video coding, especially for high and complex motion contents. The WZ video codec adopted in this study is based on a transform domain WZ video coding architecture with feedback channel-driven rate control, whose modules have been improved with some recent coding tools. This study proposes a novel motion learning approach to successively improve the rate-distortion (RD) performance of the WZ video codec as the decoding proceeds, making use of the already decoded transform bands to improve the decoding process for the remaining transform bands. The results obtained reveal gains up to 2.3 dB in the RD curves against the performance for the same codec without the proposed motion learning approach for high motion sequences and long group of pictures (GOP) sizes.

Adaptive deblocking filter for transform domain Wyner–Ziv video coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63-dB in the overall for sequences with high motion content when large group of pictures are used.

Refining Side Information for Improved Transform Domain Wyner-Ziv Video Coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, which is a recent video coding paradigm based on the Slepian-Wolf and WZ theorems. Contrary to available prediction-based standard video codecs, WZ video coding exploits the source statistics at the decoder, allowing the development of simpler encoders. Until now, WZ video coding did not reach the compression efficiency performance of conventional video coding solutions, mainly due to the poor quality of the side information, which is an estimate of the original frame created at the decoder in the most popular WZ video codecs. In this context, this paper proposes a novel side information refinement (SIR) algorithm for a transform domain WZ video codec based on a learning approach where the side information is successively improved as the decoding proceeds. The results show significant and consistent performance improvements regarding state-of-the-art WZ and standard video codecs, especially under critical conditions such as high motion content and long group of pictures sizes.

Overlapped Block Motion Estimation and Probabilistic Compensation with Application in Distributed Video Coding

It has been recently demonstrated that, in distributed video coding (DVC) side-information dependent modeling of the correlation channel brings significant performance gains over side-information independent assumptions. In this letter, we present a novel technique enabling advanced side-information dependent estimation of the correlation channel at the decoder starting from a very coarse knowledge of it. The proposed technique triggers the design of a spatial-domain DVC codec which enables the suppression of the feedback channel. Experimental results show that the proposed codec achieves similar performance compared to the state-of-the-art in transform-domain Wyner-Ziv video coding while still operating at a fraction of its encoding complexity.

Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding

In recent years, practical Wyner-Ziv (WZ) video coding solutions have been proposed with promising results. Most of the solutions available in the literature model the correlation noise (CN) between the original frame and its estimation made at the decoder, which is the so-called side information (SI), by a given distribution whose relevant parameters are estimated using an offline process, assuming that the SI is available at the encoder or the originals are available at the decoder. The major goal of this paper is to propose a more realistic WZ video coding approach by performing online estimation of the CN model parameters at the decoder, for pixel and transform domain WZ video codecs. In this context, several new techniques are proposed based on metrics which explore the temporal correlation between frames with different levels of granularity. For pixel-domain WZ (PDWZ) video coding, three levels of granularity are proposed: frame, block, and pixel levels. For transform-domain WZ (TDWZ) video coding, DCT bands and coefficients are the two granularity levels proposed. The higher the estimation granularity is, the better the rate-distortion performance is since the deeper the adaptation of the decoding process is to the video statistical characteristics, which means that the pixel and coefficient levels are the best performing for PDWZ and TDWZ solutions, respectively.