A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding

TL;DR

This paper introduces a novel computation-controlled motion estimation method for scalable video coding that adaptively allocates computational resources based on macroblock importance, maintaining high coding quality under various power constraints.

Contribution

It presents a new class-based macroblock importance measure and a complete one-pass framework for adaptive computation allocation in motion estimation.

Findings

More accurate computation allocation than previous methods

Better coding performance under fixed computation budgets

Effective in maintaining quality with reduced computation

Abstract

In this paper, a new Computation-Control Motion Estimation (CCME) method is proposed which can perform Motion Estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the Macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their class information as well as their initial matching cost information. Based on the new MB importance measure, a complete CCME framework is then proposed to allocate computation for ME. The proposed method performs ME in a one-pass flow. Experimental results demonstrate that the proposed method can allocate computation more accurately than previous methods and thus has better performance under the same computation budget.