Huanbang Chen

Bio: Huanbang Chen is an academic researcher from MediaTech Institute. The author has contributed to research in topics: Motion compensation & Affine transformation. The author has an hindex of 2, co-authored 2 publications receiving 67 citations.

An Efficient Four-Parameter Affine Motion Model for Video Coding

Abstract: In this paper, we study a simplified affine motion model-based coding framework to overcome the limitation of a translational motion model and maintain low-computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos, but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., an advanced affine motion vector prediction scheme combined with a gradient-based fast affine motion estimation algorithm and an affine model merge scheme, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation that reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation that performs motion compensation at the block level rather than the pixel level to reduce the number of interpolation. Our proposed techniques have been implemented based on the state-of-the-art high-efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve, on average, 11.1% and 19.3% bits saving for random access and low-delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both the encoder and the decoder are within an acceptable range.

An Efficient Four-Parameter Affine Motion Model for Video Coding

Abstract: In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., advanced affine motion vector prediction combined with a gradient-based fast affine motion estimation algorithm and affine model merge, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation, which reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation, which performs motion compensation at the block level rather than the pixel level to reduce the interpolation times. Our proposed techniques have been implemented based on the state-of-the-art high efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve on average 11.1% and 19.3% bits saving for random access and low delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both encoder and decoder are within an acceptable range.

Piecewise Linear Model Based Local Illumination Compensation Inter Prediction for Video Coding

Abstract: In video coding, the illumination information of video scenes is usually hard to be compressed due to the complex and unpredictable illumination variations. To simplify the problem, many prediction algorithms assume a linear correlation of illumination variations existing between frames by constructing corresponding linear model (LM), such as Weighted Prediction (WP) method. The assumption is suitable for uniform illumination variations with lager area, but ineffective in sharp illumination variations in small area. In this paper, we propose a piecewise linear model based local illumination compensation (PLMLIC) approach to further compensate sharp illumination variations in small area. When PLMLIC is applied to a coding unit (CU), we first use multiple reference lines, i.e. allow not only the nearest reference line but also long-distance reference lines to be the neighbouring samples of the current CU. Then the neighbouring samples and their corresponding reference samples are classified into 2 groups, based on which PLMLIC parameters are derived for each group. Finally, the current CU is predicted by the corresponding PLMLIC parameters. Experimental results show that 0.23% BD-rate savings on average can be achieved for lowdelay configuration based on Enhanced Compression Model (ECM) beyond VVC.

Affine motion prediction for video coding

Abstract: An example method includes obtaining, for a current block of video data, values of motion vectors (MVs) of an affine motion model of a neighboring block of video data; deriving, from the values of the MVs of the affine motion model of the neighboring block, values of predictors for MVs of an affine motion model of the current block; decoding, from a video bitstream, a representation of differences between the values of the MVs of the affine motion model for the current block and the values of the predictors; determining the values of the MVs of the affine motion model for the current block from the values of the predictors and the decoded differences; determining, based on the determined values of the MVs of the affine motion model for the current block, a predictor block of video data; and reconstructing the current block based on the predictor block.

Motion vector prediction for affine motion models in video coding

Abstract: A video decoder selects a source affine block. The source affine block is an affine-coded block that spatially neighbors a current block. Additionally, the video decoder extrapolates motion vectors of control points of the source affine block to determine motion vector predictors for control points of the current block. The video decoder inserts, into an affine motion vector predictor (MVP) set candidate list, an affine MVP set that includes the motion vector predictors for the control points of the current block. The video decoder also determines, based on an index signaled in a bitstream, a selected affine MVP set in the affine MVP set candidate list. The video decoder obtains, from the bitstream, motion vector differences (MVDs) that indicate differences between motion vectors of the control points of the current block and motion vector predictors in the selected affine MVP set.

Enhanced Bi-Prediction With Convolutional Neural Network for High-Efficiency Video Coding

Abstract: In this paper, we propose an enhanced bi-prediction scheme based on the convolutional neural network (CNN) to improve the rate-distortion performance in video compression. In contrast to the traditional bi-prediction strategy which computes the linear superposition as the predictive signals with pixel-to-pixel correspondence, the proposed scheme employs CNN to directly infer the predictive signals in a data-driven manner. As such, the predicted blocks are fused in a nonlinear fashion to improve the coding performance. Moreover, the patch-to-patch inference strategy with CNN also improves the prediction accuracy since the patch-level information for the prediction of each individual pixel can be exploited. The proposed enhanced bi-prediction scheme is further incorporated into the high-efficiency video coding standard, and the experimental results exhibit a significant performance improvement under different coding configurations.

The Joint Exploration Model (JEM) for Video Compression With Capability Beyond HEVC

Abstract: This paper provides an overview of the coding algorithms of the Joint Exploration Model (JEM) for video compression with capability beyond HEVC, which was developed by the Joint Video Exploration Team (JVET) of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). The goal of the JEM development and experimentation was to provide evidence that sufficient coding efficiency improvement over the High Efficiency Video Coding (HEVC) standard can be achieved, which would justify the need for a new video coding standard with a compression capability significantly exceeding that of HEVC. The development of the JEM provided an ability to conduct studies toward that goal in a verifiable and collaborative manner and led to the launching of the project to develop the new Versatile Video Coding (VVC) standard. Objective metric gains exceeding 30% were measured for most of the tested high-resolution video content that represents current demanding new applications, and subjective testing using human observers showed even more benefit.

An Improved Framework of Affine Motion Compensation in Video Coding

Abstract: Affine motion compensation (AMC) is a promising coding tool in Joint Exploration Model (JEM) developed by the Joint Video Exploration Team. AMC in JEM employs a 4-parameter affine model between the current block and its reference block. With this model, the motion vectors (MV) of each sub-block can be derived from the MVs at two control points. In this paper, we present a practical framework to further improve the AMC in JEM. First, we introduce a multi-model AMC approach, which allows the encoder to select either the 4-parameter affine model or the 6-parameter affine model adaptively. Second, we improve the affine inter-mode in two aspects. For the normative part, we present an efficient affine motion coding method, which replaces the affine MV Prediction (MVP) candidates in JEM with more accurate but simpler ones, and employs a second-order MVP. For the non-normative part, we enhance the motion estimation process for AMC, by regulating the optimization algorithm. Finally, we propose to unify the affine merge-mode and the normal merge-mode into a unified merge-mode, which combine affine merge candidates and normal merge candidates in a single merge candidate list. Partial of these methods have been adopted into the next generation video coding standard named Versatile Video Coding. Simulation results show that the proposed methods can achieve 1.67% BD rate savings in average for the random access configurations.