Scalable extension of HEVC with adaptive de-blocking filter for bandwidth-limited applications

Abstract

A primary latest video coding standard H.265/SHVC (Scalable High Efficiency Video Coding), an extension of High Efficiency Video Coding (HEVC) is dealt in this paper. To transfer high quality video over any network that usually occupies large bandwidth, suffers bandwidth limitation. It can be controlled by trading-off between the quality and the coding efficiency. The trade-off between these two parameters is considered in this paper to keep the difference at the minimum level. A novel Neural Network based Adaptive De-blocking Filter (NNADF) technique is proposed to maintain good PSNR with minimum bit rate in SHVC encoder. The adaptive de-blocking filter (ADF) performs block matching for nonlinear predictions to all frames in the video. Moreover, the ADF performs frame de-noising hence good quality of the video is achieved and trains a neural network (NN) model. The NN model restrains their outputs to achieve bit rate reduction, thereby coding efficiency is achieved. The simulation results show that the proposed NNADF technique delivers an average increment of 0.62 dB in BD-PSNR and an average decrement of 24 % in BD-BR for combined quality and spatial ratios respectively compared with the previously proposed algorithm.

Keywords

• H.265
• Scalable HEVC
• De-blocking filter
• BD-PSNR
• BD-BR