High Efficiency Video Coding (HEVC) provides superior coding efficiency than previous video coding standards at the cost of increasing encoding complexity. The complexity increase of motion estimation (ME) procedure is rather significant, especially when considering the complicated partitioning structure of HEVC. To fully exploit the coding efficiency brought by HEVC requires a huge amount of computations. In this paper, we analyze the ME structure in HEVC and propose a parallel framework to decouple ME for different partitions on many-core processors. Based on local parallel method (LPM), we first use the directed acyclic graph (DAG)-based order to parallelize coding tree units (CTUs) and adopt improved LPM (ILPM) within each CTU (DAGILPM), which exploits the CTU-level and prediction unit (PU)-level parallelism. Then, we find that there exist completely independent PUs (CIPUs) and partially independent PUs (PIPUs). When the degree of parallelism (DP) is smaller than the maximum DP of DAGILPM, we process the CIPUs and PIPUs, which further increases the DP. The data dependencies and coding efficiency stay the same as LPM. Experiments show that on a 64-core system, compared with serial execution, our proposed scheme achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.

Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors

High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.

A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

In recent years, the image and video coding technologies have advanced by leaps and bounds. However, due to the popularization of image and video acquisition devices, the growth rate of image and video data is far beyond the improvement of the compression ratio. In particular, it has been widely recognized that there are increasing challenges of pursuing further coding performance improvement within the traditional hybrid coding framework. Deep convolution neural network which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression. In this paper, we provide a systematic, comprehensive and up-to-date review of neural network-based image and video compression techniques. The evolution and development of neural network-based compression methodologies are introduced for images and video respectively. More specifically, the cutting-edge video coding techniques by leveraging deep learning and HEVC framework are presented and discussed, which promote the state-of-the-art video coding performance substantially. Moreover, the end-to-end image and video coding frameworks based on neural networks are also reviewed, revealing interesting explorations on next generation image and video coding frameworks/standards. The most significant research works on the image and video coding related topics using neural networks are highlighted, and future trends are also envisioned. In particular, the joint compression on semantic and visual information is tentatively explored to formulate high efficiency signal representation structure for both human vision and machine vision, which are the two dominant signal receptors in the age of artificial intelligence.

Image and Video Compression With Neural Networks: A Review

Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup.

A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

This paper describes the in-loop deblocking filter used in the upcoming High Efficiency Video Coding (HEVC) standard to reduce visible artifacts at block boundaries. The deblocking filter performs detection of the artifacts at the coded block boundaries and attenuates them by applying a selected filter. Compared to the H.264/AVC deblocking filter, the HEVC deblocking filter has lower computational complexity and better parallel processing capabilities while still achieving significant reduction of the visual artifacts.

HEVC Deblocking Filter

The lossless coding mode of the High Efficiency Video Coding (HEVC) main profile that bypasses transform, quantization, and in-loop filters is described. Compared to the HEVC nonlossless coding mode with the smallest quantization parameter value (i.e., 0 for 8-b video and -12 for 10-b video), the HEVC lossless coding mode provides perfect fidelity and an average bit-rate reduction of 3.2%-13.2%. It also significantly outperforms the existing lossless compression solutions, such as JPEG2000 and JPEG-LS for images as well as 7-Zip and WinRAR for data archiving. To further improve the coding efficiency of the HEVC lossless mode, a sample-based angular intra prediction (SAP) method is presented. The SAP employs the same prediction mode signaling method and the sample interpolation method as the HEVC block-based angular prediction, but uses adjacent neighbors for better intra prediction accuracy and performs prediction sample by sample. The experimental results reveal that the SAP provides an additional bit-rate reduction of 1.8%-11.8% on top of the HEVC lossless coding mode.

Minhua Zhou

Papers

HEVC Deblocking Filter

HEVC Lossless Coding and Improvements