Enhanced Multiple Transform for Video Coding

Zhao, Xin; Chen, Jianle; Karczewicz, Marta; Zhang, Li; Li, Xiang; Chien, Wei-Jung

doi:10.1109/dcc.2016.9

Cited by 81 publications

(36 citation statements)

References 16 publications

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“…In [17], the RDOT method is used to learn optimal sets of transforms for intra-predicted residuals in HEVC for the general case of non-separable transforms, then extended for separable transforms and Discrete Trigonometric Transforms (DTT). In this paper, set of DTTs is considered as a support to learn the transform sets used in the proposed design, in a fashion similar to the transforms adopted in JEM [7]. Hence, the RDOT learning aims at finding an optimal pair of vertical and horizontal transforms {A v , A h }, for a set of residuals {x i } defined by solving the following minimization problem:…”

Section: A Rate Distorsion Optimized Transformsmentioning

confidence: 99%

“…The first stage, called Adaptive Multiple Transforms (AMT) [7], proposes a block-level flag that signals whether the classical DCT2 (Discrete Cosine Transform kernel of type II) is used. If not, additional indexes are transmitted to signal the selected horizontal and vertical transforms, in a list of trigonometric kernels [8] (DCT and DST of types I to VIII).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement of HEVC inter-coding mode using multiple transforms

Philippe

Biatek

Lorcy³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Multiple transforms have received considerable attention recently, especially in the course of an exploration conducted by MPEG and ITU toward the standardization of the next generation video compression algorithm. This joint team has developed a software, called the Joint Exploration Model (JEM) which outperforms by over 25% the HEVC standard. The transform step in JEM consists in Adaptive Multiple Transforms (AMT) and Non-Separable Secondary Transforms (NSST) which are designed and adapted to the intra-coding modes. In intercoding, only the AMT is allowed and it is restricted to a single set of five transforms. In this paper, adaptive transforms schemes suitable for inter-predicted residuals are designed and proposed to improve the coding efficiency. Two configurations are evaluated for the proposed designs, providing an average bitrate saving of roughly 1% over HEVC with unchanged decoding time.

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Section: A Rate Distorsion Optimized Transformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of HEVC inter-coding mode using multiple transforms

Philippe

Biatek

Lorcy³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…where the quadratic term in the exponent can be decomposed in terms of graph weights (i.e., V and W) as Based on (13) and (14), it is clear that the distribution of the residual signal r depends on edge weights (W) and vertex weights (V) where • a model with larger (resp. smaller) edge weights (e.g., (W) ij ) increases the probability of having a smaller (resp.…”

Section: B Interpretation Of Graph Weights For Predictive Transform mentioning

confidence: 99%

Graph-based transforms for inter predicted video coding

Egilmez

Said

Chao

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results demonstrate that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).

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“…Recent works have shown that it is possible to gain substantially by using adaptive multiple transforms instead of a single transform. These multiple transforms are either (1) systematic fixed transforms [1], [2], (2) learned offline on a large training set [3]- [5] or (3) learned on-the-fly [6].…”

Section: Introductionmentioning

confidence: 99%

CNN-based transform index prediction in multiple transforms framework to assist entropy coding

Puri

Lasserre

Callet

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However, one needs to encode a syntax to indicate the chosen transform per residual block to the decoder for successful reconstruction of the pixels. Conventionally, the transform index is binarized using fixed length coding and a CABAC context model is attached to it. In this work, we provide a novel method that utilizes Convolutional Neural Network to predict the chosen transform index from the quantized coefficient block. The prediction probabilities are used to binarize the index by employing a variable length coding instead of a fixed length coding. Results show that by employing this modified transform index coding scheme inside HEVC, one can achieve up to 0.59% BD-rate gain.

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Enhanced Multiple Transform for Video Coding

Cited by 81 publications

References 16 publications

Improvement of HEVC inter-coding mode using multiple transforms

Improvement of HEVC inter-coding mode using multiple transforms

Graph-based transforms for inter predicted video coding

CNN-based transform index prediction in multiple transforms framework to assist entropy coding

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