Transform Domain Wyner-Ziv Video Coding 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.

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 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.

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.