Explore the vast range of titles available.

Recently, specifically designed video codecs have been preferred due to the expansion of video data in Internet of Things (IoT) devices. Context Adaptive Binary Arithmetic Coding (CABAC) is the entropy coding module widely used in recent video coding standards such as HEVC/H.265 and VVC/H.266. CABAC is a well known throughput bottleneck due to its strong data dependencies. Because the required context model of the current bin often depends on the results of the previous bin, the context model cannot be prefetched early enough and then results in pipeline stalls. To solve this problem, we propose a prediction-based context model prefetching strategy, effectively eliminating the clock consumption of the contextual model for accessing data in memory. Moreover, we offer multi-result context model update (MCMU) to reduce the critical path delay of context model updates in multi-bin/clock architecture. Furthermore, we apply pre-range update and pre-renormalize techniques to reduce the multiplex BAE’s route delay due to the incomplete reliance on the encoding process. Moreover, to further speed up the processing, we propose to process four regular and several bypass bins in parallel with a variable bypass bin incorporation (VBBI) technique. Finally, a quad-loop cache is developed to improve the compatibility of data interactions between the entropy encoder and other video encoder modules. As a result, the pipeline architecture based on the context model prefetching strategy can remove up to 45.66% of the coding time due to stalls of the regular bin, and the parallel architecture can also save 29.25% of the coding time due to model update on average under the condition that the Quantization Parameter (QP) is equal to 22. At the same time, the throughput of our proposed parallel architecture can reach 2191 Mbin/s, which is sufficient to meet the requirements of 8 K Ultra High Definition Television (UHDTV). Additionally, the hardware efficiency (Mbins/s per k gates) of the proposed architecture is higher than that of existing advanced pipeline and parallel architectures.

Scalable video coding plays an essential role in supporting heterogeneous devices, network conditions, and application requirements in modern video streaming systems. However, most existing scalable coding approaches primarily optimize human perceptual quality and provide limited support for data privacy, as well as for machine analyses and the integration of heterogeneous sensor data. This limitation motivated the development of adaptive scalable video coding frameworks. The proposed approach is designed to serve both human viewers and automated analysis systems while ensuring high security and compression efficiency. The method adaptively encrypts selected layers during transmission to protect sensitive content without degrading decoding or analysis performance. Experimental evaluations on benchmark datasets demonstrate that the proposed framework achieves superior rate distortion efficiency and reconstruction quality, while also improving machine analysis accuracy compared to existing traditional and learning-based codes. In video surveillance scenarios, where the base layer is preserved for analysis, the proposed scalable human machine coding (SHMC) method outperforms scalable extensions of H.265/High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC), reducing the average bit-per-pixel (bpp) by 26.38%, 30.76%, and 60.29% at equivalent mean Average Precision (mAP), Peak Signal-to-Noise Ratio (PSNR), and Multi-Scale Structural Similarity (MS-SSIM) levels. These results confirm the effectiveness of integrating scalable video coding with intelligent encryption for secure and efficient video transmission.

After the development of the Versatile Video Coding (VVC) standard, research on neural network-based video coding technologies continues as a potential approach for future video coding standards. Particularly, neural network-based intra prediction is receiving attention as a solution to mitigate the limitations of traditional intra prediction performance in intricate images with limited spatial redundancy. This study presents an intra prediction method based on coarse-to-fine networks that employ both convolutional neural networks and fully connected layers to enhance VVC intra prediction performance. The coarse networks are designed to adjust the influence on prediction performance depending on the positions and conditions of reference samples. Moreover, the fine networks generate refined prediction samples by considering continuity with adjacent reference samples and facilitate prediction through upscaling at a block size unsupported by the coarse networks. The proposed networks are integrated into the VVC test model (VTM) as an additional intra prediction mode to evaluate the coding performance. The experimental results show that our coarse-to-fine network architecture provides an average gain of 1.31% Bjøntegaard delta-rate (BD-rate) saving for the luma component compared with VTM 11.0 and an average of 0.47% BD-rate saving compared with the previous related work.

To achieve low-power video communication in Internet of Things, this study presents a new deep learning-based fast transcoding algorithm from distributed video coding (DVC) to high efficiency video coding (HEVC). The proposed method accelerates transcoding by minimizing HEVC encoding complexity. Specifically, it models the selections of coding unit (CU) partitions and prediction unit (PU) partition modes as classification tasks. To address these tasks, a novel lightweight deep learning network has been developed acting as the classifier in a top-down transcoding strategy for improved efficiency. The proposed transcoding algorithm operates efficiently at both CU and PU levels. At the CU level, it reduces HEVC encoding complexity by accurately predicting CU partitions. At the PU level, predicting PU partition modes for non-split CUs further streamlines the encoding process. Experimental results demonstrate that the proposed CU-level transcoding reduces complexity overhead by 45.69%, with a 1.33% average Bjøntegaard delta bit-rate (BD-BR) increase. At the PU level, the transcoding achieves an even greater complexity reduction, averaging 60.97%, with a 2.16% average BD-BR increase. These results highlight the algorithm’s efficiency in balancing computational cost and compression performance. The proposed method provides a promising low-power video coding scheme for resource-constrained terminals in both upstream and downstream video communication scenarios.

High-efficiency video coding (HEVC) is the state-of-the-art video coding standard, which adopts more complicated and time-consuming intra-prediction (IP) modes. Many-core processors are good candidates for speeding up HEVC IP in the case that HEVC IP can provide sufficient parallelism. Proposed is an efficient parallel framework for HEVC IP. Experiments show that the proposed method dramatically accelerates more than the state-of-the-art parallel method.

Video data have become the main data traffic on the Internet, and their traffic is increasing explosively every year, thus increasing the pressure of video transmission. Video coding technology has become the key to compressing original videos. As an indispensable technology, rate control plays an important role in stabilizing video stream transmission. Rate control (RC) is part of rate distortion optimization (RDO) whose job is to find the optimal solution based on balancing rate and distortion. It not only needs to consider the buffer and network status but also adjust the corresponding bit rate according to the video content. This paper reviews the related technologies of rate control under high efficiency video coding (HEVC) and versatile video coding (VVC) standards so that subsequent researchers can quickly understand the field and promote the development of rate control algorithms. Firstly, the paper summarizes the various aspects of RC, including basic principles, rate-distortion models, major processes, and performance criteria. Secondly, the paper surveys, in detail, the research progress in the field of rate control and analyzes several mainstream research directions. Thirdly, we carry out relevant experiments on the standard reference software and analyze and discuss the experimental results of the existing studies. Finally, we look ahead to the future trends of rate control and provide feasible improvement suggestions.

High-efficiency video coding (HEVC) is the next generation standard of video coding. The deblocking filter (DF) constitutes a significant part of the HEVC decoder complexity. A three-step parallel framework (TPF) is proposed for the H.264/AVC DF, which is also suitable for HEVC except the third step. The third step of the TPF is replaced with a directed acyclic graph-based order. Experiments show that the proposed method dramatically accelerates more than the state-of-the-art parallel method.

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.

Conventional video coding standards offer efficient compression of traditional 2D images. In particular, versatile video coding (VVC), which is the latest video coding standard, achieves very high compression efficiency, while maintaining high visual quality for humans. On the other hand, video coding for machines (VCM), which is developed as a new style of a video coding standard, mainly targets efficient compression of features extracted from deep neural networks. It generally employs VVC for feature coding. However, since VVC was developed for traditional images, an influence of the VVC based feature coding on VCM is not clear. Therefore, this paper proposes efficient tool combination by analyzing performance of VVC coding tools for the VCM feature coding, and then applies it into video captioning, which automatically generates natural language descriptions from videos. Experimental results show that the proposed tool combination is very efficient, in terms of coding performance and encoding complexity.

In the current high-efficiency video coding-based three-dimensional video coding (3D-HEVC) design, new depth intra modes including depth modelling modes and region boundary chain coding are applied for depth map coding. These partition-based intra modes achieve the highest possible coding efficiency, but result in extremely large encoding time which obstructs the 3D-HEVC from practical applications. An efficient early termination algorithm for depth map coding in 3D-HEVC is proposed. It makes use of the coding information from the spatial neighbouring depth map treeblock and the co-located texture video treeblock to predict the depth map intra mode treeblock and terminate its mode decision process early. Experimental results show that the proposed algorithm can achieve an average computational saving of about 40% with negligible loss of rate distortion performance in the 3D-HEVC encoder.