Inter-Block Dependency-Based CTU Level Rate Control for HEVC

Recently, the Lagrange multiplier λ based rate control has been developed in the latest HEVC (High Efficiency Video Coding) codec. It is revealed that while such a rate control approach successfully guarantees a designated bit-rate, the optimal bit allocation has not been investigated or established in the λ-domain. To enhance the overall performance, we propose a novel linear relationship between distortion and λ. With this distortion model, we deduce a closed-form solution to minimize the distortion while satisfying a given rate regulation at the coding tree unit level. Extensive simulation results show that our method can achieve an average 0.20 dB improvement in the low-delay communications.

In this paper, a novel rate control scheme for low delay video coding of High Efficiency Video Coding (HEVC) is proposed. The proposed scheme is developed by considering a new temporal prediction structure of HEVC. In the proposed scheme, the relationship between bit rate and quantization step is exploited firstly to formulate an accurate quadratic rate-quantization (R-Q) model. Secondly, a method of determining the quantization parameters (QPs) for the first frames within a group of pictures is proposed. Thirdly, an accurate frame-level bit allocation method is proposed for HEVC. Finally, based on the proposed R-Q model and the target bit allocated for the frame, the QPs are predicted for coding tree units by using rate-distortion (R-D) optimization. We compare our scheme against that of three other state-of-the-art rate control schemes. Experimental results show that the proposed rate control scheme can increase the Bjøntegaard delta peak signal-to-noise ratio by 0.65 dB and 0.09 dB on average compared with the JCTVC-I0094 and JCTVC-M0036 schemes, respectively, both of which have been implemented in an HEVC test model encoder; furthermore, the proposed scheme achieves a similar R-D performance to Wang's scheme, as well as obtaining the smallest bit rate mismatch error of all the schemes.

In this paper, we present a pixel-wise unified rate quantization (R-Q) model for a low-complexity rate control on configurable coding units of high efficiency video coding (HEVC). In the case of HEVC, which employs hierarchical coding block structure, multiple R-Q models can be employed for the various block sizes. However, we found that the ratios of distortions over bits for all the blocks are a nearly constant because of employment of the rate distortion optimization technique. Hence, one relationship model between rate and quantization can be derived from the characteristic of similar ratios of distortions over bits regardless of block sizes. Thus, we propose the pixel-wise unified R-Q model for HEVC rate control working on the multi-level for all block sizes. We employ a simple leaky bucket model for bit control. The rate control based on the proposed pixel-wise unified R-Q model is implemented on HEVC test model 6.1 (HM6.1). According to the evaluation for the proposed rate control, the average matching percentage to target bitrates is 99.47% and the average PSNR degradation is 0.76 dB. Based on the comparative study, we found that the proposed rate control shows low bit fluctuation and good RD performance, compared to R-lambda rate control for long sequences.

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.

The emerging standard of High Efficiency Video Coding (HEVC) provides a 50% improved compression performance compared to its predecessor H.264/AVC at the expense of increasing its computational complexity which is due to a large number of modes involved in the decision process where it would increase encoding time and provide hardware limitations. In this paper, the reduction of intra-coding complexity is proposed by utilization of Rate-Distortion Optimization (RDO) in preselecting a mode with the best RD cost, among all 35 intra-modes. Two cost functions – Sum of Absolute Difference (SAD) and Sum of Absolute Transform Difference (SATD, available in the HEVC Test Model (HM) Reference Software, are determined by making encoder decisions. This paper compares the performance and complexity cost of SAD only for intra-mode, with SATD-based intra-mode by modifying the HM reference encoder through forced selection of the mentioned modes. Results show that SAD consistently produces the least encoding time among all modes, along with sensible values of bitrate. Furthermore, error concealment was introduced in decoding the video sequences with the use of IEEE 802.22 Standard which only allowed power gains resulting to packet error rates (PER) ranging from 1% to 30% for the analysis on the behavior of each video sequence in an environment where error is introduced.

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.

The High Efficiency Video Coding (HEVC) standard adopts inter prediction to eliminate temporal correlation between the successive frames. However, a large amount of bits need to be explicitly signaled in the bitstream to specify the motion information. In this paper, we propose an extended skip strategy to alleviate bit consumption for motion data during the inter prediction process. Specifically, before the current frame is encoded, an additional picture generated by a deep convolutional neural network (CNN) is introduced to inter prediction. Since the additional reference picture is more similar with the current frame, most blocks of this frame can be skipped in the coding process. Consequently, to further improve the compression, an extended skip strategy is designed, i.e., the current frame can be skipped in multi-levels, including frame-level and coding tree unit level (CTU-level). Moreover, the skip-level of the current frame is decided in the sense of rate-distortion optimization (RDO). The proposed algorithm is implemented on the HM-16.6 software and an average of 4.4% BD-rate gain has been achieved in the experiments, which indicates the superiority of the proposed method.

High Efficiency Video Coding (HEVC), the latest video coding standard, aims to provide coding performance that is much superior to that of its predecessor, H.264, especially for high definition video. To fulfill this goal, the inter-prediction unit (PU) partitions of HEVC are more complex, and the search range of motion estimation (ME) is much larger. As a result, ME becomes a bottleneck in the design of the HEVC inter predictor. In response to this challenge, we developed a hardware-oriented integer ME algorithm and the related hardware implementation. Our proposed algorithm led to a decrease in terms of the Bjontegaard Delta rate when compared with the HEVC test model 15.0. The corresponding hardware solution benefitted from 2-D data reuse supported by horizontal and vertical reference SRAMs, on-chip memory reduction supported by $4\times4$ block compression, and a low-power sum of absolute difference (SAD) tree supported by PU-level chip selection. When adopting a $32\times32$ SAD tree, the minimum and maximum required working frequency for 4K $\times2\text{K}$ at 30 frames/s videos was [375, 500] MHz. These results demonstrated that our proposed solution offered desirable improvement in both coding speed and coding performance.

This paper proposes a novel approach for perceptually guiding the rate-distortion optimization (RDO) process within the High Efficiency Video Coding (HEVC) standard. The reference codec does not consider effectively the perceptual characteristics of the input video and further, the particular perceptual sensitivity of each coding tree unit (CTU) inside a frame. The corresponding frame-level Lagrangian multiplier depends only on the quantization parameter. Inspired by the mechanisms of the human visual system, the proposed solution is a CTU-Ievel adjustment of the standard Lagrangian value based on a set of complementary measured features. These measures rely on the spatial and temporal analysis of the current CTU in the frequency domain. Based on perceptual quality indices and Bjontegaard delta measurements, over several resolutions of tested video sequences, the proposed method demonstrates a promising coding performance according to the rate-distortion compromise.

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.

本文提出了一种HEVC视频编码的运动估计快速算法，该算法可以显著加速搜索过程，编码效率与HEVC参考软件(Test Model)中的Test Zone Search (TZSearch)平分秋色。较之H.264/AVC视频标准，HEVC引入了更为复杂的混合编码框架和更大尺寸的搜索窗口，从而带来了更高的计算复杂度。为了降低复杂度，我们提出了一种新的运动估计算法，该算法仅利用搜索窗中所选取的少量几个点建立二次曲面模型，通过对像素的绝对差值和(SAD)分布的迭代分析，逐步缩小搜索范围，直到得出最优的运动矢量。该算法可被用于不同的编码环境中，实验结果表明，与HM相比，该算法可以节约大约40%的运动估计算法时间，仅引起轻微的性能损失。 This paper presents a motion estimation (ME) algorithm for High Efficiency Video Coding (HEVC), which provides a strategy to speed up the search process significantly, while yielding the same quality performance as the Test Zone Search (TZSearch) scheme in HEVC Test Model (HM). Compared with the H.264/AVC, HEVC employs a more complex hybrid coding architecture and a larger size search window, leading to great computational complexity. In order to reduce the complexity, a novel motion estimation algorithm is proposed, in which some limited pixels of certain position in the current search window are utilized to build a quadratic model, and then shrink the search range repeatedly by analyzing the sum of absolute difference (SAD) distribution until the best motion vector (MV) is obtained. The proposed algorithm can be applied to various encoding conditions. Experimental results show that our method can save 40% of computations compared with the HM, with negligible decrease of coding quality.

High Efficiency Video Coding (HEVC) is a new generation video coding standard by Joint Collaborative Team on Video Coding (JCTVC). The development of HEVC aims at the compression for ultra high definition videos. In HEVC, the encoder adopts a quad-tree based video coding structure, in which each incoming frame is split into a set of coding tree blocks (CTB) for compression. Each CTB can be recursively split into coding unit (CU), predict unit (PU) and transform unit (TU). This paper proposes an early termination algorithm of recursive partition based on the Sobel edge density of CTB in HEVC intra coding mode. The proposed method can reduce the depth of recursive partition for each CTB and thus save a lot of encoding time. Experimental results by HM10.1 show that the average time savings is about 14.2% and the resultant increase of encoded BD-Rate is only 0.02%. The proposed method can achieve significant time savings with negligible BD-Rate increase.

The estimation of a model parameter is very important for coding tree unit (CTU)-level rate control as it can significantly affect bit allocation and coding performance. However, CTU-level rate control models sometimes fail as the accuracy of the model parameter is not well considered in high efficiency video coding (HEVC). Based on this observation, a model parameter estimation scheme that is based on a fast search for CTU-level rate control is proposed. First, the estimation of a model parameter is formulated as an optimization problem. Second, we transform the optimal model parameter estimation into a process to search for the most similar CTU. To improve the searching efficiency and reduce the parameter estimation error, a new spiral search mechanism is proposed to find the most similar CTU from inside to outside. According to the experimental results, the proposed method can distinctly enhance the accuracy of the CTU-level rate control and consistently outperform HM16.18 and other state-of-the-art algorithms for all testing configurations. An average reduction of 7.8% and a maximum reduction of 10.0% of the BD-Rate were achieved compared with HM16.18. An average reduction of 3.4% and a maximum reduction of 4.6% of the BD-Rate were achieved compared with other state-of-the-art algorithms. A slight computational complexity overhead is observed.

The High Efficiency Video Coding (HEVC) utilizes Z-scan order to process coding units (CUs). For intra prediction, this order cannot fully exploit the spatial correlation between adjacent CUs. After transform and quantization, the residue still contains lots of energy along edges which consumes many bits for compression. To effectively reduce the residue energy along edges, a novel intra prediction approach is proposed, where the CU processing order is changed adaptively. Two additional orders are introduced in this paper besides traditional Z-scan order. Up to 1.9% bit saving is achieved in our experiments on HEVC test model. We also propose two fast order selection algorithms and the observed gains are obtained with 27% and 2% encoding time increase compared to HEVC, respectively.

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.