Edge Density Early Termination Algorithm for HEVC Coding Tree Block

The High Efficiency Video Coding (HEVC) standard has shown large improvements in coding efficiency compared with previous standards. In particular, HEVC outperforms H.264/MPEG-4 Advanced Video Coding by up to 50% in terms of bitrate reduction for similar perceptual quality. This improvement is the result of the introduction of new coding tools, which enable the representation of data using fewer bits, but at the cost of long computation times. One of the most significant tools introduced by HEVC is the novel quadtree-based structure called the coding tree unit (CTU), which can be subsequently split into coding units (CUs), prediction units (PUs), and transform units (TUs), providing huge flexibility in the encoding. However, selecting the optimal tree partitioning requires the evaluation of a huge number of possibilities, which constitutes the most complex operation for the encoder. In order to tackle this task, this paper proposes a CTU/CU partitioning algorithm based on a pre-analysis stage. This stage performs a fast motion estimation that provides preliminary information to the encoder, including estimate distortion costs, which enables the building of the least-cost quadtree. On the basis of this tree, the evaluation performed by the encoder is restricted to a subset of PUs. After a thorough statistical analysis of numerous CU/PU combinations, we propose two different configurations that prioritize each of the target variables: coding efficiency and time reduction. In the former case, 58.09% of the encoding time can be saved at the cost of a 2.51% increase in Bjontegaard delta (BD)-rate, while a larger reduction of 63.08% is achieved with a 3.43% increase in BD-rate in the later case.

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.

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

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.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this article, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

Self-learning residual model for fast intra CU size decision in 3D-HEVC

In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video Coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.

High-Efficiency Video Coding (HEVC) is the latest video coding standard developed by the Joint Collaborative Team on Video Coding (JCT-VC). An effective rate control mechanism plays a critical role in making the best possible use of the network resources. The coding performance can be maximised through the appropriate allocation of bits under the constraints of a total bit rate budget and the buffer size. Existing rate control strategies generally determine the rate control parameters according to previous encoding results. However, there is no priori-knowledge available when the first frame of a video sequence is encoded or scene changes occur. Consequently, it is challenging to choose appropriate rate control parameters in such cases. In order to address this issue, we investigated deep learning oriented methods and proposed a Convolutional Neural Network (CNN)-based rate control scheme for HEVC intra-coding. As the CNN has the advantage of local perception and the Coding Tree Unit's (CTU's) Quantisation Parameter (QP) value is determined during the CTU level rate control process, we applied the CNN at the CTU level as well to achieve an accurate QP prediction. An improved QP determination model (R-QP model) involving only two model coefficients (µ and v) was suggested to simplify the QP calculation process. A CNN was used to predict the model coefficients, namely µ and v. Our CNN has four convolution layers, two pooling layers and three fully connected layers. Each of the convolution layers contains a Rectified Linear Unit (ReLU). The last fully connected layer outputs the predicted values of µ and v. The images from the UCID dataset and the RAISE dataset were used to train the CNN. In order to obtain a universal model, none of the frames in the HEVC common test sequences were employed as training images. In order to achieve a reasonable bit budget allocation, a CNN-based framework was used to predict the bit consumption for each CTU according to the entire picture content that it contains. At the training stage, the network took the original images and the QP values as inputs, and the corresponding actual bit consumptions were used as the labels. Once the CNN coefficients have been obtained through training, the bit consumption for each CTU under different QP values can be directly predicted from the original images. The predicted bit consumption was subsequently used to readjust the bit budget for each CTU to achieve a more reasonable bit allocation. We implemented the proposed CNN-based rate control scheme in the HEVC reference software HM 16.9. As the priori-knowledge of the previous encoding results had not been considered in the proposed algorithm, only the first frame of each video sequence was encoded. The experimental results showed that our proposed scheme performs better than the default rate control implementation in the HEVC reference software, namely the BDBR is reduced by 1.33% on average while maintaining the same reconstructed picture quality. Compared with the state-of-the-art CNN-based rate control algorithm proposed by Li, our schemes achieved better coding efficiency. It can be concluded that the proposed R-QP model is effective in describing the rate-distortion relationship in HEVC, and the CNN-based bit allocation scheme is appropriate for ensuring that the best use of each bit is made.

Multi-view video is becoming a new trend in the multimedia industry. Due to large amount of data generated by such videos, efficient compression and fast encoding speed are hot research areas. Recently MV-HEVC, the multi-view extension of High Efficiency Video Coding (HEVC) standard has been introduced. It improves the coding efficiency but the encoding complexity has increased. In this paper we propose an early coding unit (CU) splitting decision of the dependent views from the coding tree units (CTUs) depth information of the base view. Maximum depth of the CU (dependent views) is limited to the maximum value of the depths of the eight neighboring CTUs of the co-located CTU in the base view. Experimental results reveal that our proposed technique achieves upto 63.08% reduction in encoding time, when compared with HTM reference software.

In the versatile video coding (VVC) proposed by the Joint Video Exploration Team (JVET), the quad-tree with the nested multi-type tree (QTMT) partition scheme has been adopted based on the quadtree structure in the high efficiency video coding (HEVC). The video coding quality of VVC is better than the HEVC, but the algorithm complexity has also increased greatly. In this work, we present an adaptive CU split decision for intra frame with the pooling-variable convolutional neural network (CNN), targeting at various coding unit (CU) shape. The shape-adaptive CNN is realized by the variable pooling layer size where we can make the most of the pooling layer in CNN and retain the original information. Based on the proposed CNN, the CU split or not will be decided by only one trained network, same architecture and parameters for the CUs with multiple sizes. Moreover, with the proposed shape-based CNN training scheme, the various training sample size can be processed successfully. The CUbased network can avoid the full rate-distortion optimization for the CU split and the CU-level rate control can also be enabled. The experiment results show that the proposed method can save 33% coding time with only 0.99% Bjontegaard Delta bitrate (BD-rate) increase.

To satisfy the rapidly increasing demands for high resolution video, the Joint Collaborative Team on Video Coding (JCT-VC) developed a new video coding standard, i.e. High-efficiency Video Coding (HEVC). With the same perceptual video quality as H.264, HEVC can achieve a bit rate reduction of approximately 50%. However, the quad tree structured coding units (CUs) cause high computational complexity. In this paper, a novel algorithm is proposed to decrease the computational complexity of HEVC intra coding. We mainly use the total coding bits of CUs as threshold to terminate CUs' partition. Furthermore, we also consider the spatial correlation between the current LCU and its neighbor LCUs to predict the current largest CU minimum depth so that we can skip some certain CUs' rate distortion cost checking.

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.

To improve compression performance, High Efficiency Video Coding (HEVC) employs a quad-tree based block representation, namely Coding Tree Unit (CTU), which can support larger partitions and more coding modes than a traditional macroblock. Despite its high compression efficiency, the number of combinations of coding modes increases dramatically, which results in high computational complexity at the encoder. Here we propose a flexible framework for HEVC coding mode selection, with a user-defined global complexity factor. Based on linear programming, a hierarchical complexity allocation scheme is developed to allocate computational complexities among frames and Coding Units (CUs) to maximize the overall Rate-Distortion (RD) performance. In each CU, with the allocated complexity factor, a mode mapping based approach is employed for coding mode selection. Extensive experiments demonstrate that, with a series of global complexity factors, the proposed model can achieve good trade-offs between computational complexity and RD performance.

Although high efficiency video coding (HEVC) has achieved high coding performance, it has been shown that dependent rate-distortion optimization (RDO) can still further improve the coding performance at the encoder side. Inspired by our previous work on temporal-dependent RDO, this paper proposes a rate control method at coding tree unit (CTU) level to obtain both higher rate-distortion (R-D) performance and lower bitrate errors. We first formulate the global optimization problem in hybrid video coding and present a general solution framework based on a weighted Lagrange multiplier, where the weighted coefficient is related to the strength of temporal dependency among coding units, and a propagation factor is introduced for quantitative measurement of the temporal dependency. Second, an estimation method of CTU level R-D model is proposed to characterize R-D relationship more accurately. Finally, under the constraint of frame level bit budget, a formulation combining inter-block dependency and R-D characteristic is developed for optimal bit allocation at the CTU level. In the process of encoding a frame, a relaxed constraint algorithm (RCA) and a strict constraint algorithm (SCA) are employed to reduce the difference between numbers of actual and target bits for the current frame, respectively. Experimental results demonstrate that, on the HEVC test model (HM-16.7), our methods including RCA and SCA achieve considerable R-D performance improvement with lower bitrate errors, outperforming the state-of-the-art schemes. It is worth mentioning that even compared with HEVC without rate control, the RCA can still achieve bitrate savings of 5.4% and 5.1% on average under configurations of low delay P and B frames, respectively.