A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding
This work addresses efficiency challenges in video coding for applications with limited computational resources, though it appears incremental as it builds on existing motion estimation techniques.
The paper tackles the problem of performing motion estimation adaptively under varying computation or power budgets in video coding, proposing a Computation-Control Motion Estimation (CCME) method that allocates computation more accurately than previous methods, leading to better performance under the same budget.
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.