Adaptive CU Split Decision with Pooling-variable CNN for VVC Intra Encoding

High Efficiency Video Coding (HEVC), a new video coding standard currently being established, adopts a quadtree-based Coding Unit (CU) block partitioning structure that is flexible in adapting various texture characteristics of images. However, this causes a dramatic increase in computational complexity compared to previous video coding standards due to the necessity of finding the best CU partitions. In this paper, a fast CU splitting and pruning method is presented for HEVC intra coding, which allows for significant reduction in computational complexity with small degradations in rate-distortion (RD) performance. The proposed fast splitting and pruning method is performed in two complementary steps: 1) early CU split decision and 2) early CU pruning decision. For CU blocks, the early CU splitting and pruning tests are performed at each CU depth level according to a Bayes decision rule method based on low-complexity RD costs and full RD costs, respectively. The statistical parameters for the early CU split and pruning tests are periodically updated on the fly for each CU depth level to cope with varying signal characteristics. Experimental results show that our proposed fast CU splitting and pruning method reduces the computational complexity of the current HM to about 50% in encoding time with only 0.6% increases in BD rate.

This paper presents a parallel motion estimation algorithm on Graphics Processing Units (GPU) with a GPU-based fast Coding Unit (CU) splitting mechanism for speeding up the execution speed of High Efficiency Video Coding (HEVC). Parallel motion estimation algorithms only offer motion vectors to HEVC encoder, but CU splitting decision in HEVC still needs more information to speed up the encoder. Therefore, a mechanism using GPU to signify encoder which CU depth can be split instantly using motion data is designed. With experiments on Kepler GK110 GPU, the proposed parallel algorithm gains over 510 times faster than the full search motion estimation in the HEVC reference software, and the proposed fast CU splitting mechanism can further save 23% total execution time when compared to the general encoder with parallel motion estimation.

Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows great research potential. This paper proposes a VVC steganographic algorithm based on Coding Units (CUs). Considering the novel techniques in VVC, the proposed steganography only uses chroma CUs to embed secret information. Based on modifying the partition modes of chroma CUs, we propose four different embedding levels to satisfy the different needs of visual quality, capacity and video bitrate. In order to reduce the bitrate of stego-videos and improve the distortion caused by modifying them, we propose a novel convolutional neural network (CNN) as an additional in-loop filter in the VVC codec to achieve better restoration. Furthermore, the proposed steganography algorithm based on chroma components has an advantage in resisting most of the video steganalysis algorithms, since few VVC steganalysis algorithms have been proposed thus far and most HEVC steganalysis algorithms are based on the luminance component. Experimental results show that the proposed VVC steganography algorithm achieves excellent performance on visual quality, bitrate cost and capacity.

Low-complexity intra coding scheme based on Bayesian and L-BFGS for VVC

Versatile Video Coding (VVC) is the latest generation of video coding standards released to meet the arrival of the ultra-high-definition (UHD) video era. Compared with the quad-tree (QT) structure in High Efficiency Video Coding (HEVC), VVC obtains excellent coding performance by introducing the division structure of binary-tree (BT) and ternary-tree (TT), but at the same time, it also greatly improves the computational complexity in the coding process. In order to reduce the coding unit (CU) division traversal time in the inter-frame coding process, this paper proposes an algorithm for fast CU division using random forest for the VVC inter-frame coding process. The algorithm first calculates the eigenvalues of the current CU block, and then inputs them into the random forest model to obtain the predicted classification. At the same time, it decides whether to adopt the predicted result based on the decision rule. If adopted, the stack in the encoder is modified to jump over-redundant traversal operations. Experimental results show that the proposed algorithm reduces the encoding time by about 37.30% on average in random access mode, while the video quality loss is only 1.41%.

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.

Quadtree with nested multi-type tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quad-tree (QT) structure in High Efficiency Video Coding (HEVC). However, the improvement of performance leads to an un-avoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of sub-CUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and sub-parts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.

Versatile Video Coding (VVC) significantly improves the coding efficiency over the preceding high efficiency video coding (HEVC) standard, but at the expense of much higher computational complexity. Specifically for intra coding of VVC, the computational burden is mainly on the brute-force recursive rate-distortion optimization (RDO) search of quadtree with nested multi-type tree (QTMT) based coding unit (CU) partition structure. Consequently, we propose a random forest based algorithm to reduce the complexity of CU partition. The CUs are first divided into three categories, namely simple, fuzzy, and complex CUs. For simple and complex CUs, one random forest classifier is trained to directly predict the optimal partition mode. For fuzzy CUs, another random forest is trained to predict whether the partition process is terminated or not. The experimental results show that the complexity reduction of the proposed algorithm is up to 69% as compared to the VVC reference software (VTM 7.0), and averagely 57% encoding time saving is achieved with 1.21 % BDBR increase.

The rapid proliferation of audiovisual services has surpassed the capabilities of contemporary communication infrastructures, necessitating the introduction of the Versatile Video Coding (VVC) standard. VVC is the latest video coding standard, which achieves approximately a 40 % reduction in bitrate relative to its predecessor, the High Efficiency Video Coding (HEVC) standard; however, this improvement comes with a substantial increase in encoding complexity. To alleviate this burden, we propose a Vision Transformer-driven strategy for fast Coding Unit (CU) partitioning. By leveraging the transformer's global context awareness, the model swiftly predicts optimal split patterns, allowing the encoder to bypass the exhaustive rate-distortion optimization (RDO) traditionally required by VVC. This approach significantly accelerates the CU decision process while preserving compression efficiency. Unlike existing methods, the transformer model effectively captures long-range dependencies and spatial structures across the entire frame, leading to more accurate partitioning. The self-attention mechanism enables the encoder to concentrate only on promising split candidates, thereby reducing unnecessary computations. Furthermore, the proposed framework is adaptable to various video resolutions and content types, ensuring robustness and generalizability. Experimental analysis demonstrates that the proposed method achieves up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 0. 5 5 \%}$</tex> reduction in encoding time with negligible bitrate overhead and minimal degradation in visual quality, as evaluated by PSNR, SSIM, and VMAF metrics, when compared to the standalone VVC encoder.

HEVC (high efficiency video coding), as the latest video coding standard, is more efficient than H.264/AVC, nevertheless it also brings in a very high computational complexity. To reduce the time of CU (coding unit) splitting or PU (prediction unit) mode deciding, a fast algorithm based on ANN (artificial neural network) and texture analysis is proposed in this paper. First, we acquire and then label the CUs of the training set with “split” and “unsplit” according to the quad-tree CU depth. Second, the texture features of “split” and “unsplit” CUs are quantified and compiled, based on which we can set the texture thresholds to judge whether or not to split the current CU or which prediction mode should be taken preliminarily. Finally, in terms of the CUs we can't judge from texture, we use ANN or the original algorithm of HM (HEVC test model) software to decide. Compared to HM15.0, the proposed algorithm can save 51.85% encoding time on average with negligible coding efficiency loss.

High efficiency video coding (HEVC) introduces a well-designed quad-tree structure to improve coding efficiency. During quad-tree building, the encoder tries all possible coding unit (CU) partition schemes to determine the optimal one with the minimum rate distortion cost, which introduces high computation complexity. To deal with this problem, a fast algorithm based on edge information is proposed to accelerate CU splitting in this paper. Firstly, the depth and prediction mode of adjacent blocks are obtained and used to facilitate rough partition. Secondly, the edge pixel values of this CU are counted and analyzed to fine-tune the partition. Experimental results show that the proposed algorithm can reduce coding time by 35.7% on average, with an acceptable loss of Bjontegaard delta rate of 0.6%.

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.

This paper describes a video coding technology proposal submitted by MediaTek in April 2018 in response to a joint call for proposals (CfP) issued by ITU-T VCEG and ISO/IEC MPEG. The proposal uses the conventional block-based hybrid coding approach with a breakthrough design of block structure, substantial improvements of coding tools, and new inventions of loop filtering techniques. First, the block structure is designed using a highly flexible partitioning scheme, where each coding tree unit in a picture is split into multiple coding units (CUs) by a recursive quaternary tree followed by a recursive binary-ternary tree. The prediction unit (PU) and transform unit (TU) concepts are unified with the CU concept, i.e., PU and TU are basically the same as CU. Moreover, inter prediction, intra prediction, transform, quantization, in-loop filtering, and entropy coding are all improved. Finally, new loop filtering techniques are invented, where the proposed convolutional neural network loop filtering is of particular interest. The proposed video codec achieves 43.81% average bit rate savings based on objective measures compared to the High Efficiency Video Coding (HEVC) anchors and is among the best-performing CfP responses both objectively and subjectively. It has been partially adopted into the working draft of the Versatile Video Coding (VVC) standard.

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

Quad-tree-based coding unit (CU) block partitioning structure in High Efficiency Video Coding (HEVC) intra prediction causes a significant coding complexity increase. Hence, a fast block partitioning algorithm based on Support Vector Machine (SVM) is proposed in this paper. Firstly, some effective features are extracted from CUs in each depth as the input vector of SVM. Secondly, three offline trained SVM CU splitting models are loaded in each CU depth, which predict the class label of the current CU according to the extracted features. Moreover, the parameters of SVM models are resolved by grid search method. Finally, based on the predicted class label, the encoder will decide whether to split the current CU or not. Experimental results show that the proposed algorithm reduces the computational complexity of HM13.0 to 30.1% and 53.9% in encoding time with and without RDO(rate distortion optimization), while the loss in coding efficiency is negligible.