  This paper deals with the latest video coding standard H265 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 block-matching
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 times spatial ratios respectively, compared with the
existing methods.
