All zero block detection for HEVC based on the quantization level of the maximum transform coefficient

Multi-stage all-zero block detection for HEVC coding using machine learning

The latest video coding standards achieve high coding gains but result in high complexity. To reduce the computation involved and speed up the encoder, various All- Zero Block (AZB) early detection algorithms are designed to skip transform coding and quantization. In this paper, the orthogonal transformations are analyzed using the Euclid Space theory first, and then the sufficient and necessary conditions for AZB detection and non-AZB detection are derived. Based on sufficient conditions, new features for the refined classification and the corresponding thresholds are found. Finally, a general AZB detection method with a constrained false detection rate is proposed. The proposed detection scheme is applied to the 4×4 and 8×8 transforms in H.264/AVC. The experiment results show that up to 80% of the computations for transform and quantization are saved as compared to those for the encoder without AZB detection.

High Efficient Video Coding (HEVC) is the latest standard in the video transmission sector, and is designed to improve the compression rate and efficiency of video streaming applications. However, one of the many design constraints to improve HEVC is the reliability of transmission. HEVC introduces a sever output degradation when error propagation occurs because of its high compression rate. In this paper, the performance of HEVC is evaluated by video streaming over a Rayleigh distributed environment. The transmission over Rayleigh distributed environment will generate lost and dropped packets because of attenuation in the reception power and limited capacity of the network. The packet loss is then analyzed such that it obeys the statistical envelope of the propagation model used. The wireless simulation generates error patterns which depicts which frame and slice IDs in the video sequence have experienced a packet loss, which will be used as reference by the modified HEVC decoder for motion concealment. The results measured from the decoded video quality are calculated using the peak-signal-to-noise ratio (PSNR) which is compared to the percent error rate. These results were obtained by setting bit rates of 32 kbps and 128 kbps for three video sequences with different motion levels, as well as setting the bytes per slice to 16. In a wireless network with a Rayleigh distributed channel, increasing the bit rate and motion level of the video sequence will decrease the overall PSNR.

Intra prediction is an important component of video compression. The High Efficiency Video Coding (HEVC) standard supports advanced Intra prediction tools to achieve remarkable Intra coding performance. However, higher compression efficiency performance may be achieved using Combined Intra Prediction (CIP). CIP consists in combining conventional Intra reference samples, with samples extracted from within the current block being encoded. In this paper, an improvement to the original CIP approach is proposed to achieve higher compression efficiency. Due to the fact the encoder does not have access to reconstruction samples while compressing a block, a controlled drift occurs at the block level between the Intra prediction blocks generated at the encoder and decoder when using CIP. The proposed approach aims at reducing such a drift, hence improving CIP performance. This Improved CIP is able to achieve up to 1.4 % BD-rate savings with respect to the HEVC reference software. The paper also proposes the combination of Improved CIP with an additional tool for enhancing the accuracy of Intra prediction. Up to 1.9 % BD-rate savings can be achieved using this approach.

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In2N×2N PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

New video coding solutions, such as the HEVC (High Efficiency Video Coding) standard being developed by JCT-VC (Joint Collaborative Team on Video Coding), are typically designed for high resolution video content. Increasing video resolution creates two basic requirements for practical video codecs; those need to be able to provide compression efficiency superior to prior video coding solutions and the computational requirements need to be aligned with the foreseeable hardware platforms. This paper proposes an intra prediction method which is designed to provide high compression efficiency and which can be implemented effectively in resource constrained environments making it applicable to wide range of use cases. When designing the method, special attention was given to the algorithmic definition of the prediction sample generation, in order to be able to utilize the same reconstruction process at different block sizes. The proposed method outperforms earlier variations of the same family of technologies significantly and consistently across different classes of video material, and has recently been adopted as the directional intra prediction method for the draft HEVC standard. Experimental results show that the proposed method outperforms the H.264/AVC intra prediction approach on average by 4.8 %. For sequences with dominant directional structures, the coding efficiency gains become more significant and exceed 10 %.

In recent years, the applications of multimedia are rising in greedy mode and hence the amount of video transactions are also increasing exponentially. It has been shouted a great demands for researchers to develop an effective models for video encoding & decoding, compression, bandwidth allocation, transmission channel and congestion control. This research work mainly focusses on to develop an efficient video compression standard algorithm named as High Efficiency Video Coding (HEVC). It proposes a new improved Iterative based propagation update in water wave Optimization Algorithm (IPU-WWO) that optimize the weights in adopted HEVC encoding. The proposed HEVC produces double the ratio of data compression at the similar level of quality of the video and considerably enhanced video quality with same bit rate compared with Advanced Video Coding (AVC). The performance of proposed IPU-WWO is compared with other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). The experimental results show that proposed method produces better compression rate of 3.03%, 4.98%, 6.82%, and 8.38% compared with PSO, GA, FF, and ABC, respectively with high PSNR for the block size of 4. Moreover, the proposed method outperforms the state-of-the-art fast encoding algorithms for in terms of compression and performance.

High Efficiency Video Coding (HEVC), the latest video coding standard, aims to provide coding performance that is much superior to that of its predecessor, H.264, especially for high definition video. To fulfill this goal, the inter-prediction unit (PU) partitions of HEVC are more complex, and the search range of motion estimation (ME) is much larger. As a result, ME becomes a bottleneck in the design of the HEVC inter predictor. In response to this challenge, we developed a hardware-oriented integer ME algorithm and the related hardware implementation. Our proposed algorithm led to a decrease in terms of the Bjontegaard Delta rate when compared with the HEVC test model 15.0. The corresponding hardware solution benefitted from 2-D data reuse supported by horizontal and vertical reference SRAMs, on-chip memory reduction supported by $4\times4$ block compression, and a low-power sum of absolute difference (SAD) tree supported by PU-level chip selection. When adopting a $32\times32$ SAD tree, the minimum and maximum required working frequency for 4K $\times2\text{K}$ at 30 frames/s videos was [375, 500] MHz. These results demonstrated that our proposed solution offered desirable improvement in both coding speed and coding performance.

High Efficiency Video Coding (HEVC), also known as H.265, is the next generation video compression standard beyond H.264. 35 intra prediction modes(IPM) are defined in HEVC to improve coding efficiency. But so many modes also result in the dramatically increased encoding complexity. In this paper, a novel fast intra mode decision algorithm is proposed to speed up the original intra coding in HEVC. Different from previous algorithms, we analyzed the statistics generated by rough mode decision (RMD) process which is adopted in HEVC. First, we found that the modes generated by RMD are usually adjacent to each other, thus representing roughly a same direction. We merged adjacent modes into same groups. Furthermore, early termination and pixel-based edge detection methods are employed to make accurate prediction by heuristically reducing the number of candidates for rate distortion optimization (RDO) process. Experimental results show that our proposed fast intra mode decision algorithm can obtain almost 23.52% time savings in all intra low complexity cases on average with negligible performance loss compared to the original HEVC intra coding in HM7.0. Meanwhile, the results are especially favorable when it comes to high resolution test sequences with up to 0.4% bit-rate increment and 21.81% time savings.

High Efficiency Video Coding (HEVC) introduced many new coding tools to gain improved coding efficiency. However, it greatly introduces computational cost, and this will give rise to the demand for the fast algorithm that can satisfy the real-time encoding of HEVC encoder for low bandwidth environment. In this paper we introduce and evaluate a novel machine learning based approach that uses multi-view clustering to reduce the complexity of intra-coding algorithm based on the analysis of original texture and its neighborhood. The proposed approach is based on the hypothesis that intra coding mode decisions in HEVC video have a correlation with the intensities of adjacent CUs within a CTU. The proposed approach consists of two steps, coding unit size decision and reduction of candidate modes by using multi-view clustering. This results in a significant reduction in encoding time. The results show that multi-view clustering has a promising application in video encoding and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method considerably reduces encoding time with a negligible loss in quality.

In spite of the recent advances in telecommunication standards, communication networks still have limited bandwidths and storage capacity. Therefore, video compression has drawn increasing importance since high-resolution video contents have become more and more used in various fields. These requirements raise the need for high-performance video-compression technologies able to reduce the amount of data to be transmitted or stored by compressing the input video signal into a bitstream file. Improving the coding efficiency was always one of the crucial issues of various compression standards that aim to get the most compact representation of the reconstructed video, with a high subjective quality. The high-efficiency video coding (HEVC) comes to respond to these requirements. However, the increased consumption of high-quality multimedia content has pushed the international communication companies to put much effort to better enhance video-coding techniques. In this perspective, an upcoming video-coding standard to be known as versatile video coding (VVC) has emerged aiming to improve the coding efficiency of the current HEVC codec. Improvements on rate distortion (RD) performance that came with both HEVC and VVC have brought an increased complexity in the majority of the coding modules, which makes it difficult to implement on hardware systems with real-time encoding. This chapter focuses on the transform coding stage as one of the most computationally demanding modules. For HEVC, efficient approximation algorithms in addition to reconfigurable and scalable architecture have been developed in order to decrease the computational complexity of the transform module. The main objectives are to meet low power and real-time processing constraints while maintaining a compression gain and a satisfying video quality. Field programmable gate array (FPGA) implementation results and comparisons with existing works confirm the efficiency of the proposed approximations since they contribute in reducing time and power consumption, optimizing the hardware resources and bringing peak signal-to noise ratio (PSNR) improvement as well. Similarly, for the adaptive multiple transform (AMT) introduced in the transform module of the VVC, approximations were done for discrete cosine transform (DCT)-II and discrete sine transform (DST)-VII transforms since they are statistically the most used ones among the five predefined types. Bitrate (BR) reduction with a slightly degradation of video quality and a less use of hardware resources are the main contributions of the proposed approximations.

Background: The emergence of new video applications and beyond high-definition formats (e.g.,4k×2k or 8k×4k resolution) signifies the need to develop a new video coding standard that is more sophisticated than other existing standards, including H.264/AVC. The new standard must have higher coding efficiency than that of H.264/AVC. High efficiency video coding (HEVC) has been designed to address all existing applications of H.264/MPEG-4 AVC and focus on suitability for high-level aplications. Method: In this study, we compared the new HEVC standardization and the previous version (H.264/AVC). The two encoders and the different tools and algorithms used were also comprehensively investigated. Finally, a comparison between software and hardware implementation was conducted. This study provides a relevant new hardware architecture for HEVC (H.265) using the existing one for H.264/AVC. Result: Experimental results show that H.265/MPEG-HEVC provides significant average bitrate savings of 40.3% and 35.4% for interactive and entertainment applications, respectively, compared with H.264/MPEG-AVC. Keywords: Video compression, embedded systeM, HEVC, H.264/AVC, video coding standards, video CODECs Syntax overview.

Deep learning has achieved great success in the past decade, especially in the fields of computer vision and image processing. After witnessing such success, video coding experts are motivated to consider whether deep learning can also benefit video coding, and if so, they seek to discover why and how. Indeed, a number of research studies have been conducted to explore deep learning for image and video coding, which has been an active and fast-growing research area especially since the year 2015. These prior arts can be divided into two categories: new coding schemes that are built solely upon deep networks (deep schemes), and deep network-based coding tools that are embedded into traditional coding schemes (deep tools). Moreover, in the responses to the joint call for proposals on video compression with capability beyond High Efficiency Video Coding (HEVC), a number of deep tools have been proposed, and some of them are further studied for the upcoming Versatile Video Coding (VVC). In this paper, we summarize the ongoing efforts in the Joint Video Experts Team about the proposed deep tools, and we discuss several promising tools in much detail, including neural network-based intra prediction, convolutional neural network (CNN) based in-loop filtering, and CNN-based block-adaptive-resolution coding. A series of experimental results are provided to demonstrate the capability of these tools in achieving higher compression efficiency than the VVC or HEVC anchor. These results shed light on the promising direction of deep learning-based future video coding, towards which a lot of open problems call for further study.

The high compression efficiency that is provided by the high efficiency video coding (HEVC) standard comes at the cost of a significant increase of the computational load at the decoder. Such an increased burden is a limiting factor to accomplish real-time decoding, specially for high definition video sequences (e.g., Ultra HD 4K). In this scenario, a highly parallel HEVC decoder for the state-of-the-art graphics processor units (GPUs) is presented, i.e., GHEVC. Contrasting to our previous contributions, the data-parallel GHEVC decoder integrates the whole decompression pipeline (except for the entropy decoding), both for intra- and interframes. Furthermore, its processing efficiency was highly optimized by keeping the decompressed frames in the GPU memory for subsequent inter frame prediction. The proposed GHEVC decoder is fully compliant with the HEVC standard, where explicit synchronization points ensure the correct HEVC module execution order. Moreover, the GPU-based HEVC decoder is experimentally evaluated for different GPU devices, an extensive range of recommended HEVC configurations and video sequences, where an average frame rate of 145, 318, and 605 frames per second for Ultra HD 4K, WQXGA, and Full HD, respectively, was obtained in the Random Access configuration with the NVIDIA GeForce GTX TITAN X GPU.

As an extension of High Efficiency Video Coding ( HEVC), 3D-HEVC has been widely researched under the impetus of the new generation coding standard in recent years. Compared with H.264/AVC, its compression efficiency is doubled while keeping the same video quality. However, its higher encoding complexity and longer encoding time are not negligible. To reduce the computational complexity and guarantee the subjective quality of virtual views, this paper presents a novel video coding method for 3D-HEVC based on the saliency informat ion which is an important part of Human Visual System (HVS). First of all, the relationship between the current coding unit and its adjacent units is used to adjust the maximum depth of each largest coding unit (LCU) and determine the SKIP mode reasonably. Then, according to the saliency informat ion of each frame image, the texture and its corresponding depth map will be divided into three regions, that is, salient area, middle area and non-salient area. Afterwards, d ifferent quantization parameters will be assigned to different regions to conduct low complexity coding. Finally, the compressed video will generate new view point videos through the renderer tool. As shown in our experiments, the proposed method saves more bit rate than other approaches and achieves up to highest 38% encoding time reduction without subjective quality loss in compression or rendering.