Convex Optimization Based Bit Allocation for Light Field Compression under Weighting and Consistency Constraints

TL;DR

This paper introduces a convex optimization-based bit allocation method for light field image compression that accounts for weighting and visual consistency, significantly improving compression efficiency.

Contribution

It presents a novel convex optimization framework for frame-level bit allocation in light field PTS coding, considering weight and consistency constraints.

Findings

Achieves up to 24.7% BD-rate reduction.

Effectively controls distortion distribution based on weights.

Outperforms default rate control algorithms.

Abstract

Compared with conventional image and video, light field images introduce the weight channel, as well as the visual consistency of rendered view, information that has to be taken into account when compressing the pseudo-temporal-sequence (PTS) created from light field images. In this paper, we propose a novel frame level bit allocation framework for PTS coding. A joint model that measures weighted distortion and visual consistency, combined with an iterative encoding system, yields the optimal bit allocation for each frame by solving a convex optimization problem. Experimental results show that the proposed framework is effective in producing desired distortion distribution based on weights, and achieves up to 24.7% BD-rate reduction comparing to the default rate control algorithm.