Reducing correlation noise in Wyner-Ziv video coding

The research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship Scheme (Malta). The scholarship is part-financed by the European Union – European Social Fund. (ESF 1.25).Over the past few decades, Distributed Video Coding (DVC) has been considered as a compression paradigm suitable for applications which require simple encoding. Yet, the performance obtained with practical architectures is still far from the theoretical bound, mainly due to the inaccurate Side Information (SI) predicted at the decoder. The work presented in this paper tries to improve the correlation between the bit-planes of the SI and the corresponding bitplanes of the Wyner-Ziv (WZ) frame. The proposed algorithm uses the adjacent key frames to predict the quantization intervals for the SI and for the WZ frame. It then chooses whether the quantization module should use the floor or the ceiling operationsuch that theindices generated for the two frames differ by the smallest number of bits possible. Experimental results show that, for a given target quality, the proposed method can reduce the WZ bit-rate by up to 8.4% compared to the traditional coding schemes.peer-reviewe

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

TRANSFORM DOMAIN SLICE BASED DISTRIBUTED VIDEO CODING

Distributed video coding depends heavily on the virtual channel model. Due to the limitations of the side information estimation one stationary model does not properly describe the virtual channel. In this work the correlation noise is modelled per slice to obtain location-specific correlation noise model. The resulting delay from the lengthy Slepian-Wolf (SW) codec input is also reduced by reducing the length of SW codec input. The proposed solution does not impose any extra complexity, it utilizes the existing resources. The results presented here support the proposed algorithm

Selective reconstruction of low motion regions in distributed video coding

The research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship Scheme (Malta). The scholarship is part-financed by the European Union - European Social Fund. (ESF 1.25).The Distributed Video Coding (DVC) paradigm offers lightweight encoding capabilities which are suitable for devices with limited computational resources. Moreover, DVC techniques can theoretically achieve the same coding efficiency as the traditional video coding schemes which employ more complex encoders. However, the performance of practical DVC architectures is still far from such theoretical bounds, mainly due to the inaccurate Side Information (SI) predicted at the decoder. The work presented in this paper shows that the soft-input values predicted at the decoder may not correctly predict the Wyner-Ziv coefficients, even for regions containing low motion. This generally degrades compression efficiency. To mitigate this, the proposed system predicts the quality of the SI for regions with low motion and then employs a technique which avoids correcting mismatch at locations where the SI and WZ falls within different quantization intervals but the prediction error is within an acceptable range. The experimental results demonstrate that the average Peak Signal-to-Noise Ratio (PSNR) is improved by up to 0.39dB compared to the state-of-the-art DVC architectures, like the DISCOVER codec.peer-reviewe