Reducing the complexity of distributed video coding by improving the image enhancement post processing

Abstract

The main attractive feature of distributed video coding (DVC) is its use of low-complexity encoders, which are required by low-resource networked applications. Unfortunately, the performance of the currently proposed DVC systems is not yet convincing, and further improvements in the rate, distortion, and complexity tradeoff of DVC are necessary to make it more attractive for use in practical applications. This requires finding new ways to exploit side information in reducing the transmitted rate and improving the quality of the decoded frames. This paper proposes improving DVC by exploiting image enhancement post-processing at the decoder. In this way, we can either improve the quality of the decoded frames for a given rate or reduce the number of transmitted bits for the same quality and hence reduce the complexity of the encoder. To do this, we used a conditional generative adversarial network (cGAN) to restore more of the details discarded by quantization, with the help of side information. We also evaluated numerous existing deep learning-based enhancement methods for DVC and compared them to our proposed model. The results show a reduction in the number of DVC coding operations by 46 % and an improvement in rate-distortion performance and subjective visual quality. Furthermore, despite reducing its complexity, our DVC codec outperformed the DISCOVER codec with an average Bjøntegaard PSNR of 0.925 dB.