Towards robust UHD video streaming systems using scalable high efficiency video coding

Scalable High efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard High Efficiency Video Coding (HEVC). One of the key novelties introduced by SHVC is that it enables hybrid codec scalability. This basically means that the video layers can be encoded with different video standards providing backward compatibility between codecs. In this paper, we propose a software parallel SHVC decoder in hybrid codec scalability configuration. The proposed design consists of an Advanced Video Coding (AVC) decoder for the Base Layer (BL) and a HEVC decoder for the Enhanced Layer (EL). In order to perform Inter Layer Prediction (ILP), a communication of decoding states and outputs is established between the two decoders. While the native frame based parallelism is still allowed within the two decoders, the proposed design also enables the use of frame based parallelism between the two decoders. The proposed software design enables a real time decoding of the HEVC EL at 2160p60 while the AVC base layer is decoded at 1080p60 for ×2 spatial scalability.

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

H.264/AVC is regarded as a popular video coding standard, and is widely used in multimedia applications. However, with an increasing demand for better quality videos, high efficiency video coding (HEVC) is all set to serve as the successor to H.264/AVC for higher resolution video applications. Since a majority of the multimedia devices have already been operating based on the H.264/AVC standard, it may not be worthwhile to completely replace the existing software and hardware components by different modules in order to adopt HEVC in such devices. Need is therefore felt to design a decoder for supporting H.264/AVC as well as HEVC, rather than attempting individual designs. This paper introduces a new dual‐standard deblocking filter architecture, which supports both H.264/AVC and HEVC standards. Algorithmic verification has been done in Matlab and then an appropriate VLSI architecture has been implemented on FPGA as well as in ASIC domain. The proposed architecture takes 26 clock cycles for H.264/AVC and 14 cycles for HEVC to complete the filtering of a 16 × 16 pixel block. It consumes 5.80 mW normalised power and occupies an area equivalent to 70.1k equivalent gate at frequency of 100 MHz. The proposed architecture takes 8.42 ms to filter the 4K ultra high definition (UHD) (3840 × 2160) frame in H.264 standard, and it takes 18 ms to filter the 8K UHD (7680 × 4320) frame in HEVC standard.

Video communication requires sufficient resources to obtain the desired image quality. Availability of bandwidth and screen resolution from different devices on the client end, as well as compression techniques used for transmission also significantly influence to the video quality. Even though the H.264/AVC video compression standard and its scalable extension is known as Scalable Video Coding (SVC) was introduced and successfully addressed the challenge, some weaknesses persist especially for high-resolution video transmissions. The size of the bitstream generated by the SVC standard for ultra-high resolution video compression can only be met if transmitted on a network with a large bandwidth. The Scalable High-Efficiency Video Coding (SHVC) were introduced to overcome problems in the SVC standards. The new video compression standard is proven to reduce bandwidth requirements by up to 46.41 % compared to the SVC standards for the same video quality. This study introduces the performance analysis of SHVC standard video transmission as compared to SVC standard. Both compression standards are transmitted and emulated through network emulator called Mininet-Wifi to get the real transmission picture. The client-server video transmission scheme with different bandwidth and various client position are set in the experiment scenario. It is found that transmission time, packet loss, and video reconstruction quality on scalable video transmission with SHVC standard is better than scalable video transmissions with SVC standard.

In this paper, we present an efficient transmission method of multiple broadcasting services using layered division multiplexing (LDM) and scalable high efficiency video coding (SHVC) based on the next generation terrestrial digital broadcasting standard, advanced television systems committee 3.0. In a two-layer LDM system, each layer forms a physical layer pipe that carries base layer and enhancement layer video streams of SHVC, respectively. This combination of physical and presentation layers technologies can maximize channel utilization when multiple broadcasting services are delivered in a single radio frequency channel with different robustness and reception conditions. An intensive performance analysis is provided when the proposed combination of LDM and SHVC is compared with other approaches, such as time division multiplexing and/or high efficiency video coding simulcast broadcasting. Several performance measurements are also introduced, such as constant data rate measurement and constant quality measurement, which are used to evaluate the performance of video codecs in conjunction with different physical layer system parameters. A prototype hardware system with LDM and SHVC capable of selecting different parameter combinations is tested in the laboratory and in real field environments to verify the performance and feasibility of the proposed LDM and SHVC combination. Results show that the proposed LDM and SHVC combination provides significant gains on video service quality as well as reception robustness.

Telehealth applications, such as remote diagnosis and examination, have become more and more popular nowadays. However, the generated large number of medical images and their impractical digital size when it comes to telehealth have brought pressure on communication infrastructures. More specifically, the image processing time has increased dramatically due to the size of the digitized images, while requiring substantial transmission bandwidth and storage space. For these reasons, medical image compression has become a hot research topic. While image compression techniques have evolved over several generations, it remains an open question how these standards will perform when it comes to sensitive oversized medical image content. In this paper, the emerging compression standard High Efficiency Video Coding (HEVC) and the next generation standard Versatile Video Coding (VVC) are evaluated on two different medical image datasets, covering the entire range of quality levels. The results of this study show that the VVC standard overperforms HEVC on raw medical image compression both visually and quantitatively, with the performance gap narrowing when dealing with pre-compressed medical images.

Digital video applications cover today a very large range of multimedia application: messaging, HD video telephony and video conferencing. etc. These heterogeneity motivated some years ago the development of the scalable video coding. Therefore, the Joint Collaborative Team on Video Coding (JCT-VC) has developed a new Scalable High efficiency Video Coding standard (SHVC) as an extension of HEVC. The SHVC may compress numerous video sequences of various resolutions as a single bit-stream using the inter layer prediction tools. In order to highlight the features' coding efficiency; several analyses were made using numerous High Definition (HD) sequences with various scenarios. In this paper an analysis study has done on the efficiency of SHVC. Results show that multicast coding using SHVC standard provides an average bit rate reduction of more than 30% compared to simulcast. These results demonstrate the improvement in coding efficiency of the SHVC solution with the introduction of inter-layer prediction.

The latest video coding standard High Efficiency Video Coding (HEVC) is developed by Joint Collaborative Team on Video Coding(JCTVC). HEVC can achieve better compression performance than any other coding standards, which adopts the quadtree structured coding tree unit (CTU). In encoding process, the CTU is recursively split into four equally sized coding unit (CU)s. Along with the multiple sizes of the CUs, the number of prediction unit(PU) modes has been largely increased, which raises the computational complexity in HEVC encoder greatly. In this paper, a fast inter mode decision algorithm based on motion estimation (ME) and texture feature is proposed to speed up the PU mode decision in HEVC encoder. Experimental results demonstrate that the overall algorithm that is proposed can save 41% encoding time on average while the decrease of coding quality is negligible, when compared to that of the HM10.0 reference software.

Cross residual transform for lossless intra-coding for HEVC

With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user's quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately −2.85%, −8.89%, and −10.05% BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.

High Efficiency Video Coding (HEVC) is in development and aims to revolutionize the compression world with a potential 50% bitrate saving over Advanced Video Coding (AVC, or H.264 / MPEG-4 AVC). Is history repeating itself? There was a 10 year gap between the standardization of MPEG-2 Video and AVC, with AVC eventually maturing to deliver a 50% bitrate saving over MPEG-2 Video. The ITU-T|ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC) is aggressively aiming for a 2013 distribution date for the HEVC standard. Early investigations indicate there is scope for a 30–50% bitrate saving over AVC. Some may argue this is a technology without a home, but potential applications lie in IPTV over DSL where HEVC would increase service reach, or point-to-point links for newsgathering where bandwidth is at a premium. But the most applicable usages will be delivering the emerging 4Kx2K video format in the same channel capacity as HD AVC today and eventually more power-efficient coding for handheld devices. This paper explores the HEVC tool set enhancements that facilitate the compression gains over AVC, with example sequences compressed in the HM test model, and an exploration into the commercial opportunities which HEVC opens up in the television industry.

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.

The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.

In the latest video coding standard high efficiency video coding (HEVC), a quadtree-based coding unit (CU) partitioning scheme is adopted to better adapt to the characteristics of the video contents. However, the flexible scheme significantly increases the coding complexity, because large amount of possible CU partitioning modes should be traversed. In this paper, we propose a two-stage fast inter CU decision method to reduce the coding complexity of the HEVC encoders. In Stage I, all the CUs are classified into three categories based on the Bayesian method after the prediction unit (PU) mode $merge\,\,2N\times 2N$ is checked. Early CU pruning and early CU skipping are then applied to two of the categories, respectively. For the remaining category which is difficult to differentiate by the rate-distortion cost of the PU mode $merge\,\,2N\times 2N$ , an early CU pruning scheme based on conditional random fields is performed in Stage II, which takes both the local characteristics of the current CU and the coding information of its neighboring CUs into consideration. Experimental results show that our method can reduce 54.93% and 45.84% of the coding complexity on average with only 1.19% and 1.03% Bjontegaard delta bitrate increment under the random access main and the low delay P configurations, respectively.

Early detection of all-zero [formula omitted] blocks in High Efficiency Video Coding