Analysis of motion vectors and parallel computing in pseudo-sequence based light field image compression methods

Abstract

Pseudo-sequence based light field image compression methods use state-of-the-art video codecs like HEVC. Although video codecs have been designed to compress video sequences, they have good performance for light field images compression. Considering the light field images represented by their multi-views representation, a sequence with different image views is aligned following an appropriate strategy. However, there are some differences between video sequences and light field pseudo videos that can be utilized to improve the codec adaptation to the different view images compression. The pseudo-sequence images have spatial distances and predictable behaviors when compared to video sequences that have temporal distance and unpredictable behavior respectively. Considering these differences unnecessary operations can be avoided, while its performance can be improved. The video codecs compute the motion vectors using block matching motion estimation algorithms, which is computationally the most complex operation of any video codec. To reduce the motion estimation complexity many codecs use prediction models. In this paper, HEVC motion vectors search models are applied to the light field image views aligned as pseudo-sequences to analyze and find their repetitive and predictable patterns. These new patterns are then utilized for changing the HEVC motion estimation algorithm for codec complexity reduction using a state-of-the-art pseudo sequence compression method. Moreover, the use of parallel computing for the pseudo sequence compression method is addressed.