Algorithms for transform selection in multiple-transform video compression

Abstract

Selecting proper transforms for video compression has been based on the rate-distortion criterion. Transforms that appear reasonable are incorporated into a video coding system and their performance is evaluated. This approach is tedious when a large number of transforms are used. A quick approach to evaluate these transforms is based on the energy compaction property. With a proper transform, an image or motioncompensated residual can be represented quite accurately with a small fraction of the transform coefficients. This is referred to as the energy compaction property. However, when multiple transforms are used, selecting the best transform for each block that leads to the best energy compaction is difficult.In this thesis, we develop two algorithms to solve this problem. The first algorithm, which is computationally simple, leads to a locally optimal solution. The second algorithm, which is more intensive computationally, gives a globally optimal solution. We provide a detailed discussion on the ideas and steps of the algorithms, followed by the theoretical analysis of the performance. We verify that these algorithms are useful in a practical setting, by comparing and showing the consistency with rate-distortion results from previous research.We apply the algorithms when a large number of transforms are used. These transforms are equal-length 1D-DCTs in 4x4 blocks, which try to characterize as many 1D structures as possible in motion-compensation residuals. By evaluating the energy compaction property of up to 245 transforms, we quickly determine whether these transforms will bring potential performance increase in a video coding system.

Enhanced Adaptive Approach of Video Coding at Very Low Bit Rate Using MSPIHT Algorithm

“…The first algorithm, it's simple, obtains local optimal solution. The second algorithm, it's computationally intensive, obtains globally optimal solution [19].…”

Multiview video compression with 1-D transforms

“…Energy compaction can be defined as the ability to pack a large fraction of signal energy into just a few transform coefficients. The research in [3] presents the increase in energy compaction capability of the video compression system when 1D directional transforms are added for compression of MC residuals. It is reported that using 1D directional transforms and 2D DCT instead of using only 2D DCT significantly increases preserved energy.…”

Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

“…In the framework of existing hybrid video coding, the modules mainly consist of intra/inter prediction, transform, quantization, entropy encoding and in-loop filtering. To improve compression efficiency, a variety of novel coding tools have been developed in the issued standards, including QuadTree plus Multi-Type Tree (QT+MTT) structure [16] for coding block partition, Matrixbased Intra Prediction (MIP) [31] and Cross-Component Linear Model (CCLM) [43] for intra luma and chroma prediction, History-based Motion Vector Prediction (HMVP) [44] and Decoder-side MV Refinement (DMVR) [14] for motion estimation/compensation, Multiple Transform Selection (MTS) [8] for transform, CABAC engine with multi-hypothesis probability estimation for entropy encoding, Sample Adaptive Offset (SAO) and Adaptive Loop Filter (ALF) for in-loop filtering. These mentioned coding tools have achieved significant coding gains.…”