Convex Optimization-Based Bit Allocation for Video Coding

Abstract

This paper presents a new convex optimization-based bit allocation framework for video coding. Motivated by the statistical analysis of the relationship between the rate and the corresponding distortion in typical video sequences, an improved empirical rate-distortion model with more flexibility in explaining the experimental observations is proposed. The convexity and monotonicity properties of the proposed model allow us to formulate the bit allocation problem as a convex programming problem, which can be solved using interior-point methods. Coupled with the region of interest (ROI) functionality and the technique of MB classification, the proposed bit allocation scheme is then applied to the problem of frame-level bit allocation for various video coding standards employing motion compensated hybrid DCT/DPCM technique. Similar bit allocation scheme is also developed for MPEG-4 video object coding at the object level. The relevant model parameters are determined using previously encoded data by means of linear regression. Simulation results show that the proposed algorithms can achieve considerably better PSNR performance than the conventional approaches for the tested video sequences, at the expenses of slight increase in coding complexity.