A Novel Graph-Based Technique to Enhance Video Compression Algorithm

The growing popularity of high resolution video and the continuously increasing demands for high quality video on mobile devices are producing stronger needs for more efficient video encoder. Concerning these desires, HEVC, a newest video coding standard, has been developed by a joint team formed by ISO/IEO MPEG and ITU/T VCEG. Its design goal is to achieve a 50% compression gain over its predecessor H.264 with an equal or even higher perceptual video quality. Motion Estimation (ME) being as one of the most critical module in video coding contributes almost 50%-70% of computational complexity in the video encoder. This high consumption of the computational resources puts a limit on the performance of encoders, especially for full HD or ultra HD videos, in terms of coding speed, bit-rate and video quality. Thus the major part of this work concentrates on the computational complexity reduction and improvement of timing performance of motion estimation algorithms for HEVC standard. First, a new strategy to calculate the SAD (Sum of Absolute Difference) for motion estimation is designed based on the statistics on property of pixel data of video sequences. This statistics demonstrates the size relationship between the sum of two sets of pixels has a determined connection with the distribution of the size relationship between individual pixels from the two sets. Taking the advantage of this observation, only a small proportion of pixels is necessary to be involved in the SAD calculation. Simulations show that the amount of computations required in the full search algorithm is reduced by about 58% on average and up to 70% in the best case. Secondly, from the scope of parallelization an enhanced TZ search for HEVC is proposed using novel schemes of multiple MVPs (motion vector predictor) and shared MVP. Specifically, resorting to multiple MVPs the initial search process is performed in parallel at multiple search centers, and the ME processing engine for PUs within one CU are parallelized based on the MVP sharing scheme on CU (coding unit) level. Moreover, the SAD module for ME engine is also parallelly implemented for PU size of 32×32. Experiments indicate it achieves an appreciable improvement on the throughput and coding efficiency of the HEVC video encoder. In addition, the other part of this thesis is contributed to the VLSI architecture design for finding the first W maximum/minimum values targeting towards high speed and low hardware cost. The architecture based on the novel bit-wise AND scheme has only half of the area of the best reference solution and its critical path delay is comparable with other implementations. While the FPCG (full parallel comparison grid) architecture, which utilizes the optimized comparator-based structure, achieves 3.6 times faster on average on the speed and even 5.2 times faster at best comparing with the reference architectures. Finally the architecture using the partial sorting strategy reaches a good balance on the timing performance and area, which has a slightly lower or comparable speed with FPCG architecture and a acceptable hardware cost

In recent years,with the development of video coding technology and the increasing popularity of high definition(HD)video content,the ISO/IEC Moving Picture Experts Group(MPEG)and the ITU-T Video Coding Experts Group(VCEG)formed the joint collaborative team on video coding(JCV-VC)in 2010which aims to develop the next-generation video coding standard for HD video application,called High Efficiency Video Coding(HEVC).So far the test model HM has been used for performance evaluation and algorithm test.The final international standard of HEVC has been completed and published in the end of 2012based on the working plan of JCV-VC.In this paper,the key technologies about HEVC will be surveyed,specifically including the quad-tree structure of coding unit,prediction unit and transform unit,the advanced motion vector prediction and merging technology,angular intra prediction,DCT-based fractional pixel interpolation filter and context adaptive arithmetic coding etc.Finally the coding performance of some coding tools and complexity of HEVC are analyzed in detail.

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.

In this paper, a video compression standard used in digital television systems is discussed. Basic concepts of video compression and principles of lossy and lossless compression are given. Techniques of video compression (intraframe and interframe compression), the type of frames and principles of the bit rate compression are discussed. Characteristics of standard-definition television (SDTV), high-definition television (HDTV) and ultra-high-definition television (UHDTV) are given. The principles of the MPEG-2, MPEG-4 and High Efficiency Video Coding (HEVC) compression standards are analyzed. Overview of basic standards of video compression and the impact of compression on the quality of TV images and the number of TV channels in the multiplexes of terrestrial and satellite digital TV transmission are shown. This work is divided into six sections.

Detection of fake high definition for HEVC videos based on prediction mode feature

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.

High Efficiency Video Coding standard is an advanced and widely used video coding standard in recent high-definition (HD) video application. This video codec faces many challenges during the video transmission over lower bandwidth channel. One of the main challenges is the noise removal from HD video sequences. For noise removal, this video codec has in-loop filters specifically. Although these loop filters are efficient, they are not effective in removing rain pattern from the video sequences. To overcome this problem, a denoising filter has to be added to the loop filters. For denoising, an optimized multi-delay block frequency domain (OMBFD) adaptive filter is presented in this paper. Based on the intensity of raindrop, it is identified from the successive video frames. Then, these predicted raindrops are removed using the existing loop filters. Nevertheless, still, noises present in the frame are removed using the proposed OMBFD filter. To control the process of filtering, the exponential forgetting factor which is used in the proposed denoising filter is optimized using firefly optimization algorithm. Simulation results show the proposed denoising algorithm affords promising performance improvement compared to other denoising filters.

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.

High Efficient Video Coding(HEVC) is the latest generation of video compression standard after Advanced Video Coding(AVC). Which meets the video coding requirements of high-definition and high-definition video, and its real-time performance has been improved too. Inter prediction coding is one of the most frequently used methods in HEVC coding. Whether it is at the coding side or the decoding side, the motion search takes most time in the inter prediction. In the HEVC standard software HM, Test zone (TZ) Search has been used to replace the full search algorithm, which saves a lot of time on motion search, but it is still far from enough. This article proposes a fast search method based on neighbor blocks' move vector (MV). And the relationship between the Inter_2N×2N mode PU with the other modes of PU is used to judge whether narrow the search range. The experimental results show that the proposed algorithm saves about 23.5% motion estimation(ME) time and 19.37% total encoding time compared to TZ Search, and the bit consumption is basically negligible.

Depth images have different characteristics from that of color images. They usually have gradual changes within objects while steep changes happen around object boundaries. Compression standards such as H.264/AVC and High Efficiency Video Coding (HEVC) are efficient in dealing with the gradual change regions but usually result in poor performance at edge regions. To facilitate the reuse of the current video coding design and to further improve the depth compression performance, we propose a pre/post processing based compression strategy. By modifying the edge blocks in the depth image to flat blocks, the pre-processed image can be efficiently compressed using existing compression schemes. Meanwhile, those edge blocks are compressed by an edge preserving codec separately. At the decoder, the decoded modified image and the edge blocks will be merged together to form the final reconstructed image. In our simulations, we implement this strategy to HEVC to evaluate the coding performance. Experimental results show the proposed scheme can achieve about 30% - 40% bit savings for Ballet and Break dancers sequences and 60% - 70% bit savings for Kinect captured depth sequences in comparison with HEVC.

We present a study of subjective and objective quality assessment of compressed 4K ultra-high-definition (UHD) videos in an immersive viewing environment. First, we conduct a subjective quality evaluation experiment for 4K UHD videos compressed by three state-of-the-art video coding techniques, i.e., Advanced Video Coding, High Efficiency Video Coding, and VP9. In particular, we aim at investigating added values of UHD over conventional high definition (HD) in terms of perceptual quality. The results are systematically analyzed in various viewpoints, such as coding scheme, bitrate, and video content. Second, existing state-of-the-art objective quality assessment techniques are benchmarked using the subjective data in order to investigate their validity and limitation for 4K UHD videos. Finally, the video and subjective data are made publicly available for further research by the research community.

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Chapter two - Digital Image and Video Compression and Processing

Interest in playing/streaming multimedia content in Full High Definition (Full HD) and the upcoming Ultra High Definition (UHD) is rapidly increasing. However, TVs with such resolutions has higher hardware and bandwidth requirements. Hence, advanced compression tools are essential to provide TV services in Full HD and UHD quality. The aim of this paper is to explore coding efficiency performance of High Efficiency Video Coding (HEVC) and VP9 compression tools for video content in Full HD and UHD resolutions. Performance of both video compression tools is evaluated and compared with objective and subjective video quality metrics. The results show that the HEVC and VP9 codecs for Full HD and UHD videos have comparable coding efficiency. Moreover, in several cases, Quality of Experience (QoE) for Full HD, depending on the video content and target bitrate, can outperform QoE for UHD.

Currently, most video resources on line are encoded in the H.264/AVC format. More fluent video transmission can be obtained if these resources are encoded in the newest international video coding standard: high efficiency video coding (HEVC). In order to improve the video transmission and storage on line, a transcoding method from H.264/AVC to HEVC is proposed. In this transcoding algorithm, the coding information of intraprediction, interprediction, and motion vector (MV) in H.264/AVC video stream are used to accelerate the coding in HEVC. It is found through experiments that the region of interprediction in HEVC overlaps that in H.264/AVC. Therefore, the intraprediction for the region in HEVC, which is interpredicted in H.264/AVC, can be skipped to reduce coding complexity. Several macroblocks in H.264/AVC are combined into one PU in HEVC when the MV difference between two of the macroblocks in H.264/AVC is lower than a threshold. This method selects only one coding unit depth and one prediction unit (PU) mode to reduce the coding complexity. An MV interpolation method of combined PU in HEVC is proposed according to the areas and distances between the center of one macroblock in H.264/AVC and that of the PU in HEVC. The predicted MV accelerates the motion estimation for HEVC coding. The simulation results show that our proposed algorithm achieves significant coding time reduction with a little loss in bitrates distortion rate, compared to the existing transcoding algorithms and normal HEVC coding.