Mastering Quantization is Key for Video Compression

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This article discusses some key elements and solutions for efficient video compression. More precisely, we introduce key differentiating techniques for video signal quantization within modern hybrid video codecs. First, we recall the fundamentals of scalar quantization and rate-distortion (R-D) theory and then describe the different control points and levels of granularity for optimizing signal quantization in modern video compression standards. From this common knowledge, we discuss approaches in how to jointly optimize those various levels of quantization refinements for improved video encoding quality. Most notably, we explain how to take advantage of a look-ahead module (available in most industrial encoder implementations) to model spatio-temporal coding dependencies, and how to further compute optimal quantization information from an R-D standpoint for a group of pictures (GOPs). Complementary to this first approach, we share some insights about a local quantization refinement (LQR) algorithm. Such an algorithm is often ignored in practical encoder implementation due to its apparent complexity; we develop how and why it can efficiently work in realtime software encoding</i> .