Rate-distortion Optimization Process Research Articles

Rate distortion optimization with adaptive content modeling for random-access versatile video coding

In this paper, we study the capability of improving the rate-distortion (RD) performance based on the adaptive content modeling in the Versatile Video Coding (VVC) standard. In particular, the frame-level dependent relationships and inherent RD relationship are explored. As such, the rate dependency, distortion dependency and inherent RD characteristics are fully utilized in the global rate distortion optimization (RDO) process, and the quantization parameter (QP) for each frame could be adaptively solved. To facilitate the adaptive QP calculation, a two-pass coding strategy is proposed. In the first-pass coding, the video statistics are sufficiently collected with the proposed Dual Motion Compensation and Residual Coding (DMCRC) method to generate the parameters for the content-aware models. During the second-pass coding, the optimal QP at the frame level is obtained by optimizing the global RD performance with the dependent and inherent models. The proposed algorithm is implemented on VVC test model (VTM-4.0) and achieves significant performance gain for test sequences with constant and varying scenes.

Multi-Exposure Image Compression Considering Rate-Distortion Optimization in Rendered High Dynamic Range Image

High-dynamic range (HDR) images, which provide realistic visual perception, have attracted considerable attention in various applications. A simple method for obtaining HDR images is to fuse multiple low-dynamic range (LDR) images captured under varying exposures. We proposed a method of efficiently encoding multi-exposure images using multi-view High Efficiency Video Coding (MV-HEVC) along with intensity mapping function (IMF) to create high-quality HDR images. The inter-view prediction in MV-HEVC eliminates redundancy between multi-exposure images. To achieve high-efficiency MV-HEVC-based multi-exposure image coding, two modifications to the rate-distortion optimization (RDO) process performed during encoding were proposed. First, the distortion in the rendered HDR image is used to replace the original distortion, which depends on the LDR image. Second, to balance distortion and rate in RDO, a new Lagrange multiplier is derived from the derivative of the new distortion with respect to the rate. A model of the distortion in the HDR domain was constructed and expressed as a function of the distortion in the LDR domain. The proposed scheme utilizes the conventional 8-bit codec to compress multi-exposure LDR images and generate an HDR image in the decoder. The experimental results indicate that the proposed technique outperforms HDR image coding schemes in which HEVC Range Extension and JPEG-XT coding standards are used.

Fast CU Partition Decision Algorithm for VVC Intra Coding Using an MET-CNN

The newest video coding standard, the versatile video coding standard (VVC/H.266), came into effect in November 2020. Different from the previous generation standard—high-efficiency video coding (HEVC/H.265)—VVC adopts a more flexible block division structure, the quad-tree with nested multi-type tree (QTMT) structure, which improves its coding performance by 24%. However, it also causes a substantial increase in computational complexity. Therefore, this paper first proposes the concept of a stage grid map, which divides the overall division of a 32 × 32 coding unit (CU) into four stages and represents it as a structured output. Second, a multi-stage early termination convolutional neural network (MET-CNN) model is devised to predict the full partition information of a CU with a size of 32 × 32. Finally, a fast CU partition decision algorithm for VVC intra coding based on an MET-CNN is proposed. The algorithm can predict all partition information of a CU with a size of 32 × 32 and its sub-CUs in one run, completely replacing the complex rate-distortion optimization (RDO) process. It also has an early exit mechanism, thereby greatly reducing the encoding time. The experimental results illustrate that the scheme proposed in this paper reduces the encoding time by 49.24% on average, while the Bjøntegaard Delta Bit Rate (BDBR) only increases by 0.97%.

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units’ (sub-CUs’) information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).

Fast coding unit partitioning algorithms for versatile video coding intra coding

Versatile video coding (VVC) is the newest video compression standard. It adopts quadtree with nested multi-type tree (QT-MTT) to encode square or rectangular coding units (CUs). The QT-MTT coding structure is more flexible for encoding video texture, but it is also accompanied by many time-consuming algorithms. So, this work proposes fast algorithms to determine horizontal or vertical split for binary or ternary partition of a 32 × 32 CU in the VVC intra coding to replace the rate-distortion optimization (RDO) process, which is time-consuming. The proposed fast algorithms are actually a two-step algorithm, including feature analysis method and deep learning method. The feature analysis method is based on variances of pixels, and the deep learning method applies the convolution neural networks (CNNs) for classification. Experimental results show that the proposed method can reduce encoding time by 28.94% on average but increase Bjontegaard delta bit rate (BDBR) by about 0.83%.

Configurable Fast Block Partitioning for VVC Intra Coding Using Light Gradient Boosting Machine

This article presents a configurable fast block partitioning decision for Versatile Video Coding (VVC) intra-frame prediction using Light Gradient Boosting Machine (LGBM). VVC further improves the coding efficiency by introducing a Quadtree with nested Multi-Type Tree (QTMT), enabling five split types allowing square and rectangular Coding Unit (CU) sizes. However, this improvement in the coding efficiency comes at the cost of a high computational burden since several combinations of block sizes and prediction modes are evaluated through the costly Rate-Distortion Optimization (RDO) process. In this article, we propose a partitioning decision using LGBM classifiers to avoid the exhaustive RDO process and skip the evaluation of split types that are unlikely to be chosen as the best one. For this purpose, five classifiers (one for each split type) were offline trained with an efficient training process and using effective features of texture, coding, and context information. The proposed solution is highly configurable and can provide several operation points with different tradeoffs between timesaving and coding efficiency, according to the application requirements. Considering five operation points, the configurable solution can reduce the encoding time from 35.22% to 61.34%, with coding efficiency losses from 0.46% to 2.43%. Compared to the state-of-the-art, our solution is able to outperform the related works in terms of combined rate-distortion and timesaving.

High Throughput JPEG 2000 for Video Content Production and Delivery Over IP Networks

ITU-T Rec T.814 | IS 15444-15, known as High Throughput JPEG 2000, or simply HTJ2K, is Part-15 in the JPEG 2000 series of standards, published in 2019 by the ITU and ISO/IEC. JPEG 2000 Part-1 has long been used as a key component in the production, archival and distribution of video content, as the distribution format for Digital Cinema, and an Interoperable Master Format from which streaming video services are commonly derived. JPEG 2000 has one of the richest feature sets of any coding standard, including scalability, region-of-interest accessibility and non-iterative optimal rate control. HTJ2K addresses a long-standing limitation of the original JPEG 2000 family of standards: relatively low throughput on CPU and GPU platforms. HTJ2K introduces an alternative block coding algorithm that allows extremely high processing throughputs, while preserving all other aspects of the JPEG 2000 framework and offering truly reversible transcoding with the original block coded representation. This paper demonstrates the benefits that HTJ2K brings to video content production and delivery, including cloud-based processing workflows and low latency video content streaming over IP networks, considering CPU, GPU and FPGA-based platforms. For non-iterative optimal rate control, HTJ2K encoders with the highest throughputs and lowest hardware encoding footprints need a strategy for constraining the number of so-called HT-Sets that are generated ahead of the classic Post-Compression Rate-Distortion optimization (PCRD-opt) process. This paper describes such a strategy, known as CPLEX, that involves a second (virtual) rate-control process. The novel combination of this virtual (CPLEX) and actual (PCRD-opt) processes has many benefits, especially for hardware encoders, where memory size and memory bandwidth are key indicators of complexity.

A Fast CABAC Hardware Design for Accelerating the Rate Estimation in HEVC

The latest High Efficiency Video Coding standard achieves twice the coding efficiency of the H264 standard through a complex rate-distortion optimization (RDO). The coded bit-streams are produced with context adaptive binary arithmetic coding (CABAC). CABAC itself is a very time-consuming process that includes binarization, context modeling, interval subdivision, renormalization, outstanding bit handling, and context updating. The aim of this research is to speed up the CABAC process through several simplifications. First, we approximate three parts of the CABAC, i.e., interval subdivision, renormalization, and outstanding bit handling, with a piecewise-linear function that is very friendly to hardware implementation. In order to achieve better hardware parallelism, we also improve the coding process at the sub-block level. The context of syntax elements in a sub-block is redistributed to skip the complex calculation of context indexing. We perform context updating at the granularity of sub-blocks so that the data dependency of the context updating is removed completely, and the original serial encoding process is changed to a parallel encoding process. At the same time, we make another simplification for the context modeling of <i>cu&#x005F;skip&#x005F;flag</i>. Based on these simplifications, we build a parallel hardware architecture for the rate estimation of the RDO process. This architecture completes the bit estimation of a <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> coding tree unit (CTU) in 220.8 nano-seconds, whereas the Bj&#x00F8;ntegaard Delta rate increases by only 2.225&#x0025;. We believe that the proposed architecture can meet the requirements of 8K&#x0040;120 fps ultra-high-definition videos. This is the first study to simplify the hardware design of rate estimation by changing the context allocation and updating rules.

Low complexity mode selection for H.266/VVC intra coding

The newest video compression standard, called Versatile Video Coding (H.266/VVC), outperforms its predecessor High Efficiency Video Coding (HEVC) by up to 50% in the matter of coding performance. The achievement results from the integrating of several sophisticated tools such as 32 extended angular modes for intra coding, advanced partitioning structure, and additional transform selection. However, these tools negatively deliver the computational cost of the VVC Test Model (VTM) in all-intra encoding configuration. The existing fast intra coding algorithms achieve a significant saving in computational time with a comparable rate distortion (RD) performance of VVC plus additional computational complexity. In this paper, we study the original strategy of VVC intra mode decision and analyze the correlation among the sum of absolute transformed differences (SATD) costs of the rough mode decision (RMD) and the RD costs of the rate distortion optimization (RDO) processes. Based on the useful correlations with an effective threshold formulation, we propose a fast intra prediction mode selection for the most time-consuming RDO process by selecting a small candidate set based on the SATD costs and calculating the time-consuming RD costs for only intra prediction modes of the small candidate set without a significant overhead. According to the experimental results, our proposed system can reduce up to 27.38% and on average 21.07% of the computation complexity of VTM version 5.0 with an insignificant quality degradation.

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.

Rate–distortion optimization of multi-exposure image coding for high dynamic range image coding

High dynamic range (HDR) images have many practical applications because they offer an extended dynamic range and a more realistic visual experience. A HDR image is usually stored in floating-point format, so pre-processing is required to make the HDR image compatible with coding standards. A transfer function is also used to achieve better coding efficiency. Typically, HDR images are generated using several low dynamic range (LDR) images with different exposures. Instead of compressing the HDR image when it is generated from images with multiple exposures, this study proposes a technique to compress the multi-exposure images. The HDR image generation, as well as the multi-exposure images fusion, can be realized in the decoder. The proposed framework encodes the multi-exposure images using MV-HEVC where the inter-view redundancy is well exploited when an accurate intensity-mapping function between the multi-exposure images has been established. Multi-exposure image coding is used to produce a high-quality HDR image so the rate–distortion optimization (RDO) is modified by considering both the reconstruction quality of the current block and its effect on the multi-exposure fused image. A Lagrange multiplier is modified to maintain a balance between the rate and the modified distortion during the RDO process. Compared to encoding the generated HDR image using HEVC range extension, the experimental results show that the proposed technique achieves significant bitrate savings for equivalent quality in terms of HDR-VDP-2.

Task-Driven Semantic Coding via Reinforcement Learning.

Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have been studied for this purpose due to their inherent end-to-end optimization mechanism. However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process. Therefore, it is still attractive and challenging to implement task-driven semantic coding with the traditional hybrid coding framework, which should still be widely used in practical industry for a long time. To solve this challenge, we design semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Instead of directly integrating the semantic fidelity metric into traditional hybrid coding framework, we implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL). We formulate the semantic bit allocation problem as a Markov decision process (MDP) and utilize one RL agent to automatically determine the quantization parameters (QPs) for different coding units (CUs) according to the task-driven semantic fidelity metric. Extensive experiments on different tasks, such as classification, detection and segmentation, have demonstrated the superior performance of our approach by achieving an average bitrate saving of 34.39% to 52.62% over the High Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related semantic fidelity.

Improving compression efficiency of HEVC using perceptual coding

The introduction of the new coding tools in HEVC has brought significant bitrate savings compared to the previous standard, H.264/AVC. In both standards, the mean square error (MSE) is used for measuring distortion in the rate distortion optimization process of the coding unit structure and mode selection. However, MSE is not a good measure to use for measuring visual quality as it poorly correlates with human perception. Integration of a video quality metric based on the characteristics of the Human Visual System (HVS) inside the rate distortion optimization procedure is expected to improve the compression efficiency of video coding. In this paper, the PSNR-HVS measure is used in the rate distortion optimization process for the coding unit structure and mode selection. In the first step, we find the scaling factor for the Lagrangian multiplier based on the proposed perceptual approach. In the second step, we find optimal Lagrangian multiplier depending on the quantization parameter. The compression efficiency of the proposed approach is compared to that of HEVC. Simulations prove that the proposed approach yields higher compression efficiency.

Perceptual coding scheme for ultra-high definition video based on perceptual noise channel model

Perceptual video coding (PVC) can eliminate visual redundancy and effectively improve the compression performance of ultra-high definition video. The conventional PVC schemes, involving the perceptual characteristics related to the video features, do not account for interference in the human visual system caused by external environment changes. Therefore, this paper presents a PVC scheme based on a perceptual noise channel model. In this approach, visual dither is utilized to imitate the adjustment process of human eyes while environment changes. Considering reliable external visual stimulus decoding in the perceptual noise channel and visual characteristics, dynamic estimation of visual dither quantization is performed based on a message passing algorithm on a factor graph. Furthermore, a weight factor based on the perceptual noise channel capacity is incorporated into the rate distortion optimization process. The experimental results show that the proposed PVC scheme achieves significant bitrate reduction with better subjective video quality than competitive PVC schemes.

Rate-distortion-complexity optimization for x265

Rate-distortion optimization (RDO) is conventionally based on the analysis of rate-distortion (R-D) curve to minimize the coding distortion under the coding bits constraint. However, it is necessary to consider the computational complexity in the RDO process. In this paper, we obtain the Confidence LEvel - Computational complexity (CLEC) curves which indicate the characteristics of coding tree units (CTUs). Based on the CLEC curves, a rate-distortion-complexity optimization (RDCO) algorithm is proposed to optimize R-D under given computational complexity and achieve the optimal coding performance for x265. Experimental results demonstrate that the proposed algorithm can achieve a wide range of encoding speed under a given quantization parameter (QP) whereas the original x265 can only achieve a few fixed encoding speeds, and the proposed algorithm can reduce the BD-rate and increase the BD-PSNR by 6.59% and 0.13 dB on average under the same requirements of encoding speeds as the original x265.

Occupancy-Map-Based Rate Distortion Optimization and Partition for Video-Based Point Cloud Compression

The video-based point cloud compression (V-PCC) is the state-of-the-art dynamic point cloud compression method developed by the Moving Pictures Experts Group (MPEG). It projects the point cloud patch by patch to its bounding box and organizes all the patches into a video to utilize the efficient video coding framework. However, the unoccupied pixels among different patches will lead to inefficiency of the video compression. First, the unoccupied pixels are treated equal to the occupied pixels in the rate distortion optimization process. However, the unoccupied pixels are useless for the reconstructed quality of the point cloud. Second, the edges of the occupied and unoccupied pixels can divide a coding unit into arbitrary shapes. Consequently, they are not well-characterized by the regular partitions supported in the video coding framework. Therefore, we propose using occupancy-map-based rate distortion optimization and partition to deal with these two problems. First, the occupancy map is used as a mask to ignore the distortions of the unoccupied pixels when calculating the rate distortion cost of a specified block. This strategy is applied to both intra prediction, inter prediction, and the sample adaptive offset to boost the performance. Second, we propose an occupancy-map-based partition to divide the occupied pixels from different patches and the unoccupied pixels into different prediction units (PUs). The motions of occupied PUs are then predicted using the auxiliary information. The unoccupied PU is finally padded using the approach that generates the geometry frames. The proposed algorithms are implemented in the V-PCC and High Efficiency Video Coding reference software. The experimental results show that the algorithms can individually contribute significant performance improvements compared with the V-PCC. Additionally, their combination can achieve even more bitrate savings than the sum of the individual algorithms.

Fast Depth and Mode Decision in Intra Prediction for Quality SHVC.

Scalable High Efficiency Video Coding (SHVC) is the extension of High Efficiency Video Coding (HEVC). In intra prediction for quality SHVC, a Coding Unit (CU) is recursively divided into a quadtree-based structure from the largest 64×64 CU to the smallest 8×8 CU, in which 35 intra prediction modes and Inter-Layer Reference (ILR) mode are checked to determine the best possible mode. This leads to very high coding efficiency but also results in an extremely high coding complexity. To improve coding speed while maintaining coding efficiency, in this paper, we propose a new efficient algorithm for fast intra prediction for enhancement layer in SHVC. First, temporal and spatial correlations, as well as their correlation degrees, are combined in a Naive Bayes classifier to predict depth probabilities and skip depths with low likelihood. Second, for a given depth candidate, we combine ILR mode probability with Partial Zero Blocks (PZBs) based on the Sum of Squared Differences (SSD) to determine whether the ILR mode is the best one. In that case, we can skip intra prediction, which requires very high complexity. Third, initial Intra Modes (IMs) are obtained through Sobel operator, and are combined with the relationship between IMs and their corresponding Hadamard Cost (HC) values to predict candidate IMs in Rough Mode Decision (RMD). Then, an analytical criterion of early termination is developed based on the HC values of two neighboring IMs in the Rate-Distortion Optimization (RDO) process. Finally, we combine depth probabilities and the distribution of residual coefficients at the current depth to early terminate depth selection. The proposed scheme can significantly decrease the complexity of depth determination while reducing the complexity of mode decision for a depth candidate. Our experimental results demonstrate that the proposed scheme can achieve a speed up gain of more than 80% in average, while maintaining coding efficiency.

Video Coding Optimization for Virtual Reality 360-Degree Source

To provide excellent visual experience for customers, virtual reality (VR) sources require higher resolutions and better visual quality than traditional picture sequences. The content of a VR video can be mapped into a sphere by playing devices to present a 360° scene, which is usually called VR360 in industrial community. The most popular formats for VR360 sources are the equirectangular projection (ERP) and the cubemap projection (CMP). Both ERP and CMP pictures can be effectively projected to a virtual three-dimensional spherical surface for rendering. It brings a new challenge to the compression of VR video sources, which is how to reallocate proper bit-rate to match mainstream projection formats. The most intuitive way to deal with this challenge is to empirically assign a fixed quantization parameter (QP) to each coding unit according to its position, which evidently lacks precision, rationality, and thus, degrades coding performance. This research proposes a new entropy equilibrium optimization (EEO) methodology to enhance the coding performance of VR360 videos. Specifically, we develop a spherical bit-rate equalization strategy to obtain a block-level Lagrangian multiplier (lambda, λ) for the rate-distortion optimization process in video coding. The appropriate QP value for each block is then dynamically determined in accordance with its λ. Based on our EEO methodology, we develop two algorithms, EEOA-ERP and EEOA-CMP, to enhance compression efficiency for the ERP and CMP pictures, respectively. Experimental results demonstrate that both algorithms achieve significant BD-Rate savings and outperform the HM16.17 platform for all-intra (AI), low-delay (LD) and random-access (RA) configurations, respectively. Concretely, compared with the state-of-the-art algorithm WSU-ERP, the proposed EEOA-ERP achieves BD-Rate saving of 0.37% in LD configuration. Furthermore, the proposed EEOA-CMP gains 2.6% on objective quality in RA configuration when compared with the HM16.17 VR CMP under the common test condition.

Perceptual Video Coding Scheme Using Just Noticeable Distortion Model Based on Entropy Filter

Because perceptual video coding (PVC) can reduce bitrates with negligible visual quality loss in video compression, a PVC scheme based on just noticeable distortion (JND) model is proposed for ultra-high definition video. Firstly, the proposed JND model is designed, considering the spatial JND characteristics such as contrast sensitivity, luminance adaptation and saliency weight factor. Secondly, in order to perform precise JND suppression, the Gauss differential entropy (GDE) filter is designed to divide the image into smooth and complex texture region. Thirdly, through incorporating the proposed JND model into the encoding process, the transform coefficients are suppressed in harmonization with the transform/quantization process of high efficiency video coding (HEVC). In order to achieve the JND suppression effectively, a distortion compensation factor and distortion compensation control factor are incorporated to control the extent of distortion in the rate distortion optimization process. The experimental results show that the proposed PVC scheme can achieve a remarkable bitrate reduction of 32.98% for low delay (LD) configuration and 28.61% for random access (RA) configuration with a negligible subjective quality loss. Meanwhile, the proposed method only causes about average 12.94% and 22.45% encoding time increase under LD and RA configuration compared with an HEVC reference software, respectively.