HEVC Intra Coding Research Articles

Perceptual Quality Consistency Oriented CTU Level Rate Control for HEVC Intra Coding

Rate control is an important technique for practical video coding applications by allocating suitable quantization parameters (QPs) for CTUs or frames to satisfy bandwidth requirement. However, rate control may result in video quality fluctuation because neighboring regions are quantized into different quality levels, which may affect viewing experience seriously, especially at low bitrate scenarios. In this paper, we propose a novel CTU (Coding Tree Unit) level rate control approach for intra frame of the high efficiency video coding (HEVC) standard with consistent perceptual quality. In particular, a new perceptual hyperbolic rate distortion (R-D) optimization method is proposed for rate control with the constraint of constant perceptual quality, which shows good compatibility with other R-D models. Herein, a novel CTU level perceptual distortion model is presented to achieve the optimal rate control of intra frames for HEVC, where the Lagrange multiplier is adjusted by taking advantage of the different perceptual characteristics to achieve the minimal perceptual distortion variation across CTUs within one frame. Extensive experiments prove that the proposed algorithm outperforms the state-of-the-art CTU level rate control methods by considering R-D performance. In addition, subjective simulation results demonstrate that our algorithm further improves the visual quality of compressed videos by reducing perceptual quality fluctuation.

End-to-End Optimized 360° Image Compression.

The 360° image that offers a 360-degree scenario of the world is widely used in virtual reality and has drawn increasing attention. In 360° image compression, the spherical image is first transformed into a planar image with a projection such as equirectangular projection (ERP) and then saved with the existing codecs. The ERP images that represent different circles of latitude with the same number of pixels suffer from the unbalance sampling problem, resulting in inefficiency using planar compression methods, especially for the deep neural network (DNN) based codecs. To tackle this problem, we introduce a latitude adaptive coding scheme for DNNs by allocating variant numbers of codes for different regions according to the latitude on the sphere. Specifically, taking both the number of allocated codes for each region and their entropy into consideration, we introduce a flexible regional adaptive rate loss for region-wise rate controlling. Latitude adaptive constraints are then introduced to prevent spending too many codes on the over-sampling regions. Furthermore, we introduce viewport-based distortion loss by calculating the average distortion on a set of viewports. We optimize and test our model on a large 360° dataset containing 19,790 images collected from the Internet. The experiment results demonstrate the superiority of the proposed latitude adaptive coding scheme. On the whole, our model outperforms the existing image compression standards, including JPEG, JPEG2000, HEVC Intra Coding, and VVC Intra Coding, and helps to save around 15% bits compared to the baseline learned image compression model for planar images.

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion.

Mismatches between the precisions of representing the disparity, depth value and rendering position in 3D video systems cause redundancies in depth map representations. In this paper, we propose a highly efficient multiview depth coding scheme based on Depth Histogram Projection (DHP) and Allowable Depth Distortion (ADD) in view synthesis. Firstly, DHP exploits the sparse representation of depth maps generated from stereo matching to reduce the residual error from INTER and INTRA predictions in depth coding. We provide a mathematical foundation for DHP-based lossless depth coding by theoretically analyzing its rate-distortion cost. Then, due to the mismatch between depth value and rendering position, there is a many-to-one mapping relationship between them in view synthesis, which induces the ADD model. Based on this ADD model and DHP, depth coding with lossless view synthesis quality is proposed to further improve the compression performance of depth coding while maintaining the same synthesized video quality. Experimental results reveal that the proposed DHP based depth coding can achieve an average bit rate saving of 20.66% to 19.52% for lossless coding on Multiview High Efficiency Video Coding (MV-HEVC) with different groups of pictures. In addition, our depth coding based on DHP and ADD achieves an average depth bit rate reduction of 46.69%, 34.12% and 28.68% for lossless view synthesis quality when the rendering precision varies from integer, half to quarter pixels, respectively. We obtain similar gains for lossless depth coding on the 3D-HEVC, HEVC Intra coding and JPEG2000 platforms.

Orthogonal Directional Transforms using Discrete Directional Laplacian Eigen Solutions for Beyond HEVC Intra Coding.

We introduce new transforms for efficient compression of image blocks with directional preferences. Each transform, which is an orthogonal basis for a specific direction, is constructed from an eigen-decomposition of a discrete directional Laplacian system matrix. The method is a natural extension of the DCT, expressing the Laplacian in Cartesian coordinates rotated to some predetermined angles. Symmetry properties of the transforms over square domains lead to efficient computation and compact storage of the directional transforms. A version of the directional transforms was implemented within the beyond HEVC software and demonstrated significant improvement for intra block coding.

Statistical Early Termination and Early Skip Models for Fast Mode Decision in HEVC INTRA Coding

In this article, statistical Early Termination (ET) and Early Skip (ES) models are proposed for fast Coding Unit (CU) and prediction mode decision in HEVC INTRA coding, in which three categories of ET and ES sub-algorithms are included. First, the CU ranges of the current CU are recursively predicted based on the texture and CU depth of the spatial neighboring CUs. Second, the statistical model based ET and ES schemes are proposed and applied to optimize the CU and INTRA prediction mode decision, in which the coding complexities over different decision layers are jointly minimized subject to acceptable rate-distortion degradation. Third, the mode correlations among the INTRA prediction modes are exploited to early terminate the full rate-distortion optimization in each CU decision layer. Extensive experiments are performed to evaluate the coding performance of each sub-algorithm and the overall algorithm. Experimental results reveal that the overall proposed algorithm can achieve 45.47% to 74.77%, and 58.09% on average complexity reduction, while the overall Bjøntegaard delta bit rate increase and Bjøntegaard delta peak signal-to-noise ratio degradation are 2.29% and −0.11 dB, respectively.

Fast CU Size Decision for HEVC Intra Coding by Using Local Characteristics and RD Costs

Fast CU Size Decision for HEVC Intra Coding by Using Local Characteristics and RD Costs

Fast Coding Unit Size Decision in HEVC Intra Coding

Fast Coding Unit Size Decision in HEVC Intra Coding

Fast Algorithm Based on Rough LCU Minimum Depth Prediction and Early CU Partition Termination for HEVC Intra Coding

Fast Algorithm Based on Rough LCU Minimum Depth Prediction and Early CU Partition Termination for HEVC Intra Coding

Fast Coding Unit Decision Algorithm for HEVC Intra Coding

Fast Coding Unit Decision Algorithm for HEVC Intra Coding