An enhanced performance for H.265/SHVC based on combined AEGBM3D filter and back-propagation neural network

Abstract

This paper deals with the latest video coding standard H.265/ SHVC, a scalable extension to High Efficiency Video Coding (HEVC). HEVC introduces new coding tools compared to its predecessor and is backward compatible with all types of electronic gadgets. The gadgets with different display capabilities cannot be offered the same quality video due to the constraints in transmission bandwidth is a major problem. One solution to this problem will be the compression of the video sequence which is focused in this paper to preserve or increase PSNR while reducing bit-rate besides a novel method implemented in SHVC encoder. The novel method undergoes a combined AEGBM3D (adaptive edge guided blockmatching and 3D) filtering and back-propagation technique. The technique includes an AEGBM3D filter which avoids spatial redundancy and de-noise frames; hence enhancement in PSNR is achieved. The obtained PSNR of the video is compared with the set threshold PSNR to maintain PSNR above the threshold by repeated AEGBM3D filtering. The BP technique based on the neural network machine learning approach continually restrains the output if the input block does not contain a feature they were trained to recognize. This frequent control over the output produces few bits; hence reduction in bit-rate is achieved. The simulation results show that the proposed technique delivers an average increment of 0.16 and 0.25dB in PSNR and an average decrement of 28 and 37% in bit-rate for ×1.5 and ×2 spatial ratios, respectively, compared with the existing methods.