Rate-distortion Optimization Process Research Articles

End User Video Quality Prediction and Coding Parameters Selection at the Encoder for Robust HEVC Video Transmission

Along with the rapid increase in the availability of high-quality video formats such as high definition (HD), ultra HD, and high dynamic range, a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec high efficiency video coding (HEVC) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/advanced video coding, the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers’ perceived quality degradation prior to transmitting HEVC coded videos over an error-prone network, and to include error resilient features so as to minimize the adverse effects of those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP– $\lambda $ relationship which can be used in the rate-distortion optimization (RDO) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames, and the clipping operations are accounted, and consequently, the model is capable of minimizing the prediction error to as low as 3.11%, whereas the state-of-the-art methods cannot reach below 20.08%, under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%–43.59% improvement in the BD-rate compared to the state-of-the-art error resilient algorithms.

A multi-metric approach for block-level video quality assessment

Developing an objective video quality metric that accurately estimates perceived video quality is challenging. Developing a metric that can additionally be embedded in the rate distortion optimization process of a video codec can be even harder given that decisions have to be made locally. In this paper, we present a method for combining a number of existing state of the art objective video quality metrics at the coding block level by employing a fusion of local content features for deciding how to best utilize the chosen metrics. Our results indicate promising performance in terms of the correlation of the developed locally-acting quality metric with the overall perceived quality of the video.

Run-Time Deep Learning Enhanced Fast Coding Unit Decision for High Efficiency Video Coding

The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured coding unit (CU) to enhance the coding efficiency. However, the computational complexity significantly increases because of the exhaustive rate-distortion (RD) optimization process to obtain the optimal coding tree unit (CTU) partition. In this paper, we propose a fast CU size decision algorithm to reduce the heavy computational burden in the encoding process. In order to achieve this, the CU splitting process is modeled as a three-stage binary classification problem according to the CU size from [Formula: see text], [Formula: see text] to [Formula: see text]. In each CU partition stage, a deep learning approach is applied. Appropriate and efficient features for training the deep learning models are extracted from spatial and pixel domains to eliminate the dependency on video content as well as on encoding configurations. Furthermore, the deep learning framework is built as a third-party library and embedded into the HEVC simulator to speed up the process. The experiment results show the proposed algorithm can achieve significant complexity reduction and it can reduce the encoding time by 49.65%(Low Delay) and 48.81% (Random Access) on average compared with the traditional HEVC encoders with a negligible degradation (2.78% loss in BDBR, 0.145[Formula: see text]dB loss in BDPSNR for Low Delay, and 2.68% loss in BDBR, 0.128[Formula: see text]dB loss in BDPSNR for Random Access) in the coding efficiency.

Adaptive CU Mode Selection in HEVC Intra Prediction: A Deep Learning Approach

The computational time of HEVC encoder is increased mainly because of the hierarchical quad-tree-based structure, recursive coding units, and the exhaustive prediction search up to 35 modes. These advances improve the coding efficiency, but result in a very high computational complexity. Furthermore, selecting the optimal modes among all prediction modes is necessary for subsequent rate-distortion optimization process. Therefore, we propose a convolution neural network-based algorithm which learns the region-wise image features and performs a classification job. These classification results are later used in the encoder downstream systems for finding the optimal coding units in each of the tree blocks, and subsequently reduce the number of prediction modes. The experimental results show that our proposed learning-based algorithm reduces the encoder time saving up to 66.89% with a minimal Bjontegaard delta bit rate (BD-BR) loss of 1.31% over the state-of-the-art machine learning approaches. Furthermore, our method also reduces the mode selection by 45.83% with respect to the HEVC baseline.

Pixel-Level View Synthesis Distortion Estimation for 3-D Video Coding

Recently, region-based 3D video coding has been proposed. However, existing view synthesis distortion estimation (VSDE) methods are performed at the frame level. To guide the rate-distortion optimization process of region-based 3D video coding schemes, this paper proposes the first pixel-level VSDE (PL-VSDE) method. We first give the definition of the pixel-level view synthesis distortion. To estimate it, a backward prediction method is then developed, which starts from the pixels of interest (POIs) in the virtual view and finds their corresponding pixels in the reference view via a coarse-to-fine approach, denoted as coarse-to-fine backward prediction (CFBP) method. Additionally, the CFBP fully considers the details of 3D warping, the rounding operation and the warping competition in view synthesis, leading to improve accuracy of the prediction. Besides, a table-lookup method and a warping property are introduced to speed up the CFBP. After integrating the CFBP into the PL-VSDE, we can estimate the view synthesis distortion at the pixel level. Our method is carried out pixel-by-pixel independently, which is friendly for parallel processing. The experimental results demonstrate that our proposed method has significant advantages in both accuracy and efficiency compared with the state-of-the-art frame-level VSDE methods.

Probabilistic Approach Versus Machine Learning for One-Shot Quad-Tree Prediction in an Intra HEVC Encoder

Evolutions of the Internet of Things (IoT) in the next years are likely to boost mobile video demand to an unprecedented level. A large number of battery-powered systems will integrate an Hevc video codec, implementing the latest encoding MPEG standard, and these systems will need to be energy efficient. Constraining the energy consumption of Hevc encoders is a challenging task, especially for embedded applications based on software encoders. The most efficient approach to reduce the energy consumption of an Hevc encoder consists in optimizing the quad-tree block partitioning of the image and trade-off compression efficiency and energy consumption by efficiently choosing the near-optimal pixel block sizes. For the purpose of reducing the energy consumption of a real-time Hevc Intra encoder, this paper proposes and compares two methods that predict the quad-tree partitioning in “one-shot”, i.e. without iterating. These methods drastically limit the computational cost of the recursive Rate-Distortion Optimization (RDO) process. The first proposed method uses a Probabilistic approach whereas the second method is based on Machine Learning approach. Experimental results show that both methods are capable of reducing the energy consumption of an embedded Hevc encoder of 58% for a bit rate increase of respectively 3.93% and 3.6%.

On predicting the HEVC intra quad-tree partitioning with tunable energy and rate-distortion

Future evolutions of the Internet of Things (IoT) are likely to boost mobile video demand to an unprecedented level. A large number of battery-powered systems will then integrate an High Efficiency Video Coding (Hevc) codec, implementing the latest video encoding standard from MPEG, and these systems will need to be energy efficient. Constraining the energy consumption of Hevc encoders is a challenging task, especially for embedded applications based on software encoders. The most efficient approach to manage the energy consumption of an Hevc encoder consists of optimizing the quad-tree partitioning and balance compression efficiency and energy consumption. The quad-tree partitioning splits the image into encoding units of variable sizes. The optimal size for a unit is content dependent and affects the encoding efficiency. Finding this optimal repartition is complex and the energy required by the so-called rate-distortion optimization (RDO) process dominates the encoder energy consumption. For the purpose of budgeting the energy consumption of a real-time Hevc encoder, we propose in this paper a variance-aware quad-tree prediction that limits the energetic cost of the RDO process. The predictor is moreover adjustable by two parameters, (Δ––,Δ¯¯¯¯), offering a trade-off between energetic gains and compression efficiency. Experimental results show that the proposed energy reduction scheme is able to reduce the energy consumption of a real-time Hevc encoder by 45–62% for a bit rate increase of, respectively, 0.49 and 3.4%. Moreover, the flexibility offered by parameters (Δ––,Δ¯¯¯¯) opens new opportunities for energy-aware encoding management.

Multilevel optimal λ decision for low-delay rate control in high-efficiency video coding

By considering the rate distortion optimization (RDO) characteristics, a multilevel optimal λ decision algorithm for the rate control is proposed. With the process of getting the optimal λ for the group of picture (GoP)-level rate control, the frame-level rate control, and the coding tree unit (CTU)-level rate control, a special relationship of the current level λ and the next level λ can be built. First, an approximate linear bit allocation relationship is used between the bit rate and the motion compensation prediction error to assign the bit rate more reasonably for every frame. Second, the multilevel optimal λ decision scheme is proposed. To minimize the whole distortion of a sequence, an effective way is to get the optimal RDO of every GoP. Then, with the RDO process of the GoP-level rate control, an optimal frame λ ratio for each frame in current GoP can be obtained. For the optimal frame λ ratio, its value is mainly decided by the distortion effect, which is calculated by an employed source distortion propagation chain. Finally, an optimal CTU λ clip scheme is proposed to get the optimal RDO of every CTU. The experimental results show that the coding quality of the proposed rate control algorithm is improved significantly with little loss of the bit rate accuracy.

Fast intra coding algorithm for HEVC based on depth range prediction and mode reduction

The latest High Efficiency Video Coding (HEVC) standard only requires 50% bitrate of the H.264/AVC at the same video quality. Due to introduction of more advanced coding tools and techniques, its computational complexity rises rapidly. In this paper, we propose a novel fast Intra coding algorithm. Firstly, a novel feature is proposed to measure the complexity of video content based on the analysis of mode information obtained from a previous frame. Then, a model is built based on the relationship between this feature and Coding Tree Unit (CTU) depth range. According to the model, the unnecessary operations of Coding Unit (CU) split are skipped. Secondly, the correlation between CU distribution structure and mode selection is established. For the prediction of unlikely CU, the last few intra modes in candidate list are eliminated before rate-distortion optimization process. Experimental results demonstrate that the proposed algorithm reduces the encoding time by 43.2% with just 0.47% BD-rate increase compared with HEVC test model. Compared with other state-of-art algorithms, the proposed algorithm achieves better trade-off between complexity reduction and RD performance.

In-loop perceptual model-based rate-distortion optimization for HEVC real-time encoder

In this paper, a novel High Efficiency Video Coding (HEVC)-compliant perceptual rate-distortion optimization (RDO) scheme is proposed based on motion attention and visual distortion sensitivity models, which both fully utilize in-loop coding information of HEVC. In detail, the motion attention model is designed by using the motion vectors (MVs) estimated during the inter-prediction process. The MV field is refined based on maximum a posteriori (MAP) estimation to remove MV outliers and improve the model’s efficiency. In addition, the visual distortion sensitivity is modeled by using the spatiotemporal energy of AC coefficients, which are obtained from HEVC transform process. Then, these two models are incorporated together into the RDO process. As a result, the Lagrange multiplier and quantization parameter are adjusted adaptively in an analytical way. Since the two models are calculated within the HEVC coding loop, the complexity increase is limited. The experimental results indicate that the proposed perceptual RDO scheme can achieve significantly better rate-VQM performance than the conventional RDO scheme. Specifically, the BD-rate can reach a maximum 24.45% and an average 13.68% reduction in terms of the Bjontegaard Delta metric compared to HEVC practical encoder x265.

Weighted Rate-Distortion Optimization for Screen Content Coding

Unlike camera-captured video, screen content (SC) often contains a lot of repeating patterns, which makes some blocks used as references much more important than others. However, conventional rate-distortion optimization (RDO) schemes in video coding do not consider the dependence among image blocks, which often leads to a locally optimal parameter selection, especially for SC. In this paper, we present a weighted RDO scheme for SC coding (SCC), in which the repeating characteristics are taken into account when deciding RD tradeoff for each block. For one block, the number being referenced by the current picture and following pictures is estimated and based on the number, we set a proper weight in the RDO process to reflect its importance from a global point of view. To estimate the number being referenced, we propose a hash-based method to approximate the results to avoid the complexity of direct search. Experimental results show that compared with the High Efficiency Video Coding SCC reference software, 10.1%, 14.5%, and 2.2% on average and up to 25.7%, 39.8%, and 4.6% bit saving can be achieved by considering weights provided by our scheme for hierarchical-B, IBBB, and all intra coding structures, respectively. Thanks to our hash-based design, the complexity increase brought by the proposed scheme is marginal.

Source Distortion Temporal Propagation Analysis for Random-Access Hierarchical Video Coding Optimization

Due to the widely used inter prediction in the current video coding standards, encoding units in different frames is of temporal dependency in that the rate-distortion optimization (RDO) of one unit may affect the coding performance of the following units in the temporal domain. To achieve optimal coding solution for a given video sequence, temporal dependency among units needs to be considered in the RDO process, which is known as the temporally dependent RDO (TD-RDO). The hierarchical coding structure (HCS) employed in the High Efficiency Video Coding (HEVC) standard further complicates this problem by grouping frames into different layers of varying coding strategies, leading to a more complex temporal relationship. In our earlier work, we addressed TD-RDO for the low delay HCS (LD-HCS), where only uni-prediction is considered. This paper aims to address more complicated TD-RDO under random access HCS (RA-HCS), where both uni-prediction and bi-prediction are considered, making the temporal relationship even more intricate. The temporal dependency introduced in the RA-HCS is thoroughly examined and an RA-based TD-RDO scheme is formulated for each layer by modeling temporal propagation of distortion under different prediction types. Based on the formulation, the global Lagrange multiplier can be obtained analytically. Moreover, the effect of random access point pictures is considered in the RA-based TD-RDO scheme. The proposed method can be simply realized by updating the Lagrange multiplier as in the independent RDO formulation or combined with adjusting quantization parameter (QP) for better results in terms of BD-rate saving. Experimental results show that under RA-HCS, the proposed method, by adapting the Lagrange multiplier only, can achieve about 2.2% bitrate savings in average. With multi-QP optimization, an average BD-rate gain of 5.2% can be obtained.

Perceptually Adaptive Lagrangian Multiplier for HEVC Guided Rate-Distortion Optimization

Recent video coding standards typically use the Rate-distortion optimization (RDO) method, which is essential to appropriately perform mode decisions during encoding process. The newest standard high efficiency video coding (HEVC) introduces complex encoding structures and strong dependency between coding units. Particularly, the Lagrangian multiplier is a primary factor in RDO procedure, which directly affects the rate-distortion (R-D) performance and is defined for an entire video frame. This paper proposes a novel approach for perceptually guiding the RDO process in HEVC. The reference encoder does not consider effectively the perceptual characteristics of the input video and further, the visual sensitivity of each coding tree unit (CTU) in a frame. Inspired by the mechanisms of the human visual system, the proposed solution is a CTU-level adjustment of Lagrangian value based on a set of complementary perceptual features. The proposed scheme concerns important visual information of a CTU and its temporal dependency with adjacent blocks. Feature extraction is implemented in the frequency domain using efficient spatio-temporal analysis. In our experiments, we opted a perceptual mean squared error (MSE) metric and structural similarity (SSIM) index. According to perceptual MSE metric, the BD-rate savings using the Bjontegaard delta measurements, were fairly convincing over the state-of-the-art HEVC software HM16.12; 4.41% and 6.14% for random access (RA) and low delay (LD) encoding settings, respectively. Using SSIM, the BD-Rate achieved 6.95% and 9.86% for RA and LD settings, respectively. The proposed method further demonstrates a superior R-D performance over a compared approach adopting a similar scheme.

Perceptual rate distortion optimization of 3D–HEVC using PSNR-HVS

Improved compression efficiency is highly desirable in the transmission of 3D video and its storage. 3D–HEVC achieves higher compression efficiency compared to the simulcast HEVC or disparity-compensated multi-view video coding (MVC). In 3D–HEVC, the mean square error (MSE) is used to measure distortion in the rate distortion optimization process. However, MSE is not a good measure to use for measuring visual quality, as it poorly correlates with human perception. We propose to integrate a perceptual video quality metric inside the rate distortion optimization process of the 3D–HEVC. Specifically, in the coding unit (CU) mode selection process, PSNR-HVS is used as a measure for distortion. PSNR-HVS is based on the characteristics of the human visual system (HVS). Performance evaluations show that the proposed approach improves the compression efficiency of 3D–HEVC for multi-view videos by 2.78%.

Fast intra prediction method by adaptive number of candidate modes for RDO in HEVC

The intra prediction of high efficiency video coding (HEVC) selects the candidate modes through rough mode decision (RMD) among 35 modes for intra coding. The fixed number of candidate modes after the RMD might be inefficient, because the rate-distortion optimization (RDO) process using candidate modes still takes about 35% of total encoding time.In this letter, we propose an efficient method to reduce the number of candidate modes adaptively by adjusting the threshold for Hadamard cost (HC). The threshold is determined using the weight based on the standard deviation of gray level of pixels in prediction unit. The proposed method performs the RDO process using the reduced number of candidate modes having smaller HC than the threshold. Simulation results show that the proposed method gives about 16% complexity reduction with just 0.08% BD-rate increase compared with the original HEVC test model. The proposed method gives significant improvement of BD-PSNR and BD-rate with similar complexity reduction compared with the previous state-of-the-art method.

Fast intra coding based on CU size decision and direction mode decision for HEVC

High Efficiency Video Coding (HEVC) adopts a quad-tree structured coding unit (CU) and more prediction unit (PU) modes are used in all CU depth levels. These improvements bring better coding performance, but the exhaustive search process for optimal CU and PU selection causes higher computational complexity than earlier standards. To speed up the encoder, this paper proposes a fast intra coding algorithm. Firstly, a fast CU size decision method is introduced, which selects different depth decision methods for each largest coding unit. Secondly, a fast direction mode decision method is proposed. It first compares the direction modes of the parent unit and most probable modes (MPMs) list, and then the first direction mode of rate-distortion optimization (RDO) list is used to early terminate the RDO process. Experimental results show that compared with HM 10.0, the proposed algorithm achieves 47% time reduction, whereas the value of BDBR is only 0.7%.

Review of Fast Mode Decision Algorithms for Intra Prediction in High Efficiency Video Coding Standard

The emerging High Efficiency Video Coding (HEVC) standard is a new improved next generation video coding standard. HEVC aims to provide improved compression performance as compared to all other video coding standards. To improve the coding efficiency a number of new techniques have been used. The higher compression efficiency is obtained at the cost of an increase in the computational load. In HEVC 35 modes are provided for intra prediction to improve the compression efficiency. The best mode is selected by Rate Distortion Optimization (RDO) process. It achieves significant improvement in coding efficiency compared with previous standards. However, this causes high encoding complexity. This paper discuss the various fast mode decision algorithms for intra prediction in HEVC.

Fast CU size and prediction mode decision algorithm for HEVC based on direction variance

As the latest video coding standard, High Efficiency Video Coding (HEVC) achieves high coding performance, up to 50% bit rate savings compared with the previous standard, H.264/Advanced Video Coding while maintaining the same output video quality. Intra-coding in the HEVC significantly improves the compression efficiency, due to the various kinds of coding unit (CU) sizes, and high density of angular prediction modes. However, the improvement of the coding performance is obtained at the expense of the extraordinary computation complexity, which obstructs the usefulness of HEVC encoder for real-time applications. This paper presents a novel intra-encoding algorithm for HEVC, which is composed of an efficient CU partitioning technique and a fast intra-mode decision method based on direction variance. We utilize a preprocessing method in which the texture complexity and direction energy distribution along a special direction are extracted from a macroblock. According to the texture complexity obtained, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower-dimensions CUs or not. Furthermore, a subset of prediction modes in accordance with the dominant direction energy distribution is chosen for the further rough mode decision and rate–distortion optimization process. The simulation results indicate the algorithm based on image understanding achieves better trade-off between rate–distortion performance and complexity reduction than the previous algorithms. Compared with the reference software HM16.7, the proposed algorithm can save 57% coding time on average, with a negligible bit rate increase of 0.65%, and quality losses lower 0.08 dB, respectively.

Towards an FPGA-Based HEVC Encoder: A Low-Complexity Rate Distortion Scheme for AMVP

Advanced motion vector prediction (AMVP) is a new technique adopted in the latest high efficiency video coding (HEVC) standard. AMVP block predicts an initial motion vector of the current block from a given set of candidates by means of rate distortion (RD) optimization process. Due to the large number of different-sized blocks, simplification of RD optimization process in AMVP block is highly appreciated. Therefore, we present a new RD optimization technique for AMVP block in HEVC encoder. The proposed RD calculation approach finds the best AMVP candidate by processing less number of feature pixels per every block. Experimental results show notable speedup in terms of AMVP processing time with tolerable quality degradation (PSNR) and bitrate requirement. The proposed RD calculation technique reduces the RD computational complexity of the AMVP block by 87.5% as maximum (i.e. 1.7% of the whole encoder complexity). This improvement is accompanied with a modest average PSNR loss of 0.10 dB and an increase by 2.4% in terms of bitrate. On the other hand, we present an FPGA-based architecture for AMVP unit in HEVC encoder. The proposed architecture was prototyped, simulated and synthesized on Xilinx Virtex-7 XC7VX550T FPGA. At 188 MHz clock frequency, the proposed architecture processes 8 K (7680 \(\times \) 4320) YCrCb resolution at 60 fps while utilizing less than 1% of the FPGA resources.

A fast inter-frame encoding scheme using the edge information and the spatiotemporal encoding parameters for HEVC

High Efficiency Video Coding (HEVC), as a novel video coding standard, has shown a better coding efficiency than all existing standards, such as H.264/AVC. It adopts a lot of new efficient coding tools, the most important one is the new hierarchical structures which include the coding unit (CU), prediction unit (PU) and transform unit (TU). However, the rate-distortion (RD) optimization process for all CUs, PUs, and TUs cause large computational costs. In this paper, a fast Inter-frame encoding scheme using the edge information and the spatiotemporal encoding parameters is proposed to reduce the encoder complexity of HEVC, which consists of a fast all 2 N × 2 N modes decision method, a fast CU depth level decision method and a fast PU mode decision method. This scheme uses edge information to express the structure complexity and uses the difference of edge information between current CU and its spatiotemporal CUs to express the edge similarity (ES) in one frame and the edge movement (EM) between two adjacent frames. And then, utilizes ES and EM as assistant parameters cooperate with CU depth levels and PU mode RD costs of spatiotemporal CUs to accomplish the early termination of CU split and the PU mode selection. The experimental results show that the proposed fast inter-frame encoding method can significantly reduce the computational costs with negligible RD loss. There are 53.7 and 54.9% encoding time savings on average, but only with average 1.5 and 1.8% Bjontegaard difference bitrate (BDBR) losses for various test sequences under random access and low delay conditions, respectively.