An Introduction to High Efficiency Video Coding Range Extensions

High Efficiency Video Coding (HEVC) is the latest international video coding standard, which can provide the similar quality with about half bandwidth compared with its predecessor, H.264/MPEG?4 AVC. To meet the requirement of higher bit depth coding and more chroma sampling formats, range extensions of HEVC were developed. This paper introduces the coding tools in HEVC range extensions and provides experimental results to compare HEVC range extensions with previous video coding standards. Ex?perimental results show that HEVC range extensions improve coding efficiency much over H.264/MPEG?4 AVC High Predictive profile, especially for 4K sequences.

Architecture of Intra Prediction for High Efficiency Video Coding

This paper explains intra prediction method for High Efficiency Video Coding(HEVC).Intra prediction removes correlation of adjacent samples in spatial domain.Intra predictor requires reference images which are stored in external memory.Memory access is required frequently in process of intra prediction.The proposed architecture can reduce external memory access by optimized internal buffer.

基于块编码特点的压缩视频质量增强算法

针对现有压缩视频质量增强算法未能充分利用压缩视频特点的问题,研究了视频编码与压缩视频质量增强任务之间的本质关系,并针对性地设计了一种基于三维卷积神经网络(3D convolutional neural network, 3D-CNN)的非对齐压缩视频质量增强算法。实验结果表明:相较于高效视频编码(high efficiency video coding, HEVC)标准H.265,所提算法在低延迟(low delay, LD)配置下且量化参数(quantization parameter, QP)为37时,峰值信噪比(peak signal-to-noise ratio, PSNR)提升了0.465 2 dB;相较于数据压缩会议(data compression conference, DCC)中提出的多帧引导的注意力网络(multi-frame guided attention network, MGANet)方法,该算法PSNR的增长量提升了15.1%。

Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding(HEVC)frames and improves its subjective visual quality in multimedia services over communication networks.However,on faster processing of the complex videos at a low bitrate,some visible artifacts considerably degrade the picture quality.In this paper,we proposed a four-step fuzzy based adaptive deblocking filter selection technique.The proposed method removes the quantization noise,blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate.We have considered Y(luma),U(chromablue),and V(chroma-red)components parallelly.Finally,we have developed a fuzzy system to detect blocking artifacts and use adaptive filters as per requirement in all four quadrants,namely up 45◦,down 45◦,up 135◦,and down 135◦across horizontal and vertical block boundaries.In this context,experimentation is done on a wide variety of videos.An objective and subjective analysis is carried out with MATLAB software and Human Visual System(HVS).The proposed method substantially outperforms existing postprocessing deblocking techniques in terms of YPSNR and BD_rate.In the proposed method,we achieved 0.32–0.97 dB values of YPSNR.Our method achieved a BD_rate of+1.69%for the luma component,−0.18%(U)and−1.99%(V)for chroma components,respectively,with respect to the stateof-the-art methods.The proposed method proves to have low computational complexity and has better parallel processing,hence suitable for a real-time system in the near future.

Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots

The latest video coding standard High Efficiency Video Coding （HEVC） can achieve much higher coding efficiency than previous video coding standards. Particularly, by exploiting the hierarchical B-picture prediction structure, temporal redundancy among neighbor frarnes is eliminated remarkably well. In practice, videos available to consumers usually contain many repeated shots, such as TV series, movies, and talk shows. According to our observations, when these videos are encoded by HEVC with the hierarchical B-picture structure, the temporal correlation in each shot is well exploited. However, the long-term correlation between repeated shots has not been used. We propose a long-term prediction （LTP） scheme to use the long-term temporal correlation between correlated shots in a video. The long-term reference （LTR） frames of a source video are chosen by clustering similar shots and extracting the representative frames, and a modified hierarchical B-picture coding structure based on an LTR frame is introduced to support long-term temporal prediction. An adaptive quantization method is further designed for LTR frames to improve the overall video coding efficiency. Experimental results show that up to 22.86% coding gain can be achieved using the new coding scheme.

A simplified hardware-friendly contour prediction algorithm in 3D-HEVC and parallelization design

After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To reduce the computational complexity of DMM-4,a simplified hardware-friendly contour prediction algorithm is proposed in this paper.Based on the similarity between texture and depth map,the proposed algorithm directly codes depth blocks to calculate edge regions to reduce the number of reference blocks.Through the verification of the test sequence on HTM16.1,the proposed algorithm coding time is reduced by 9.42%compared with the original algorithm.To avoid the time consuming of serial coding on HTM,a parallelization design of the proposed algorithm based on reconfigurable array processor(DPR-CODEC)is proposed.The parallelization design reduces the storage access time,configuration time and saves the storage cost.Verified with the Xilinx Virtex 6 FPGA,experimental results show that parallelization design is capable of processing HD 1080p at a speed above 30 frames per second.Compared with the related work,the scheme reduces the LUTs by 42.3%,the REG by 85.5%and the hardware resources by 66.7%.The data loading speedup ratio of parallel scheme can reach 3.4539.On average,the different sized templates serial/parallel speedup ratio of encoding time can reach 2.446.

Reconfigurable design of deblocking filter for variable block sizes

Based on the flexible quadtree partition structure of coding tree units(CTUs),the deblocking filter(DBF)in high efficiency video coding(HEVC)consumes a lot of resources when implemen-ted by hardware.It is difficult to achieve flexible switching between different sizes of coding blocks.Aiming at this problem,a reconfigurable implementation of DBF is proposed.Based on the dynamic programmable reconfigurable video array processor(DPRAP)with context switch reconfiguration mechanism,the runtime flexible switching of two coding block sizes is realized.The experimental results show that the highest work-frequency reaches 151.4 MHz.Compared with the dedicated hardware architecture scheme,the resource consumption can be reduced by 28.1%while realizing the dynamic switching between algorithms of two coding block sizes.Compared with the results of HM16.0,by using a complete I-frame for testing,the average peak signal-to-noise ratio(PSNR)of the reconfigurable implementation proposed in this paper has increased by 3.0508 dB,the coding quality has improved to a certain extent.

Portable Perceptron Network-Based Fast Mode Decision for Video-Based Point Cloud Compression

In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression parameters need to be determined based on the specific quality requirement.In high efficiency video coding(HEVC),regarded as the state-of-the-art compression tool,the quantization parameter(QP)plays a dominant role in controlling quality.The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results.In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control.Its kernel is a support vector regression(SVR)based learning model that is capable of predicting the optimal QP from both vid-eo-based and structural image features extracted directly from raw data,avoiding time-consuming processes such as pre-encoding and iteration,which are often needed in existing techniques.Experimental results on several datasets verify that our approach outperforms current video-based quality control methods.