Approaches for video compression using tensor based compact representation

Abstract

Video compression is reducing the storage space of the video contents by exploiting redundancy or by compact representation using some transforms The primary objective of this research is to develop video compression methods using tensor as a compact representation and apply different operators and tensor decomposition methods to achieve a high compression rate and enhanced video quality during reconstruction The secondary objective is to reduce consumption of the bit rate due to dynamic textures Three novel video compression methods using tensor representation are proposed and compared with state of the art methods and standards such as H 264 AVC and H 265 HEVC The objective of the first method is to develop a video compression framework with a high compression rate and superior video quality Hence a Low Multi Linear Rank Approximation LMLRA of a tensor is used for decomposition to compress videos that are represented as 4D tensors Thus the encoder has blocks i for a multi linear rank approximation method to aid decomposition ii for sparsity removal of residual data to reduce the memory storage required to the bare minimum iii to quantize sparse less dense residual data and iv to offer LZ77 dictionary coding for videos He decoder uses a tensor reconstruction block along with a residual error correction block to reduce losses/errors in reconstructed videos The size of the core tensor is a key factor in rank approximation and is identified at the run time depending on the video content The tensor size is determined adaptively using Tikhonov s regularization method The best value of the core tensor size is identified with the corner of the L curve to preserve video contents from heavy losses errors newline