Improved Method to Select the Lagrange Multiplier for Rate-Distortion Based Motion Estimation in Video Coding

• Published in: IEEE transactions on circuits and systems for video technology 2014-03, Vol.24 (3), p.452-464
• Main Authors: Gonzalez-de-Suso, Jose Luis, Jimenez-Moreno, Amaya, Martinez-Enriquez, Eduardo, Diaz-de-Maria, Fernando
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Circuit properties; Coders; Coding; Computer programs; Detection, estimation, filtering, equalization, prediction; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Encoders; Encoding; Estimation; Exact sciences and technology; H264/Advanced Video Coding (AVC); Image processing; Information, signal and communications theory; Lagrange multiplier; Lagrange multipliers; motion estimation; Motion segmentation; Optimization; Partitions; rate-distortion optimization; Signal and communications theory; Signal convertors; Signal processing; Signal, noise; Software; Telecommunications and information theory; Video coding
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2013.2276857

The motion estimation (ME) process used in the H.264/AVC reference software is based on minimizing a cost function that involves two terms (distortion and rate) that are properly balanced through a Lagrangian parameter, usually denoted as λmotion. In this paper we propose an algorithm to improve the conventional way of estimating λmotion and, consequently, the ME process. First, we show that the conventional estimation of λmotion turns out to be significantly less accurate when ME-compromising events, which make the ME process to perform poorly, happen. Second, with the aim of improving the coding efficiency in these cases, an efficient algorithm is proposed that allows the encoder to choose between three different values of λmotion for the Inter 16x16 partition size. To be more precise, for this partition size, the proposed algorithm allows the encoder to additionally test λmotion=0 and λmotion arbitrarily large, which corresponds to minimum distortion and minimum rate solutions, respectively. By testing these two extreme values, the algorithm avoids making large ME errors. The experimental results on video segments exhibiting this type of ME-compromising events reveal an average rate reduction of 2.20% for the same coding quality with respect to the JM15.1 reference software of H.264/AVC. The algorithm has been also tested in comparison with a state-of-the-art algorithm called context adaptive Lagrange multiplier. Additionally, two illustrative examples of the subjective performance improvement are provided.