Rate-distortion Optimization Research Articles

Recursive Residual Convolutional Neural Network-Based In-Loop Filtering for Intra Frames

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original noncompressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.

RHEVC intra‐prediction mode

Although the high-efficiency video codec plays a leading role among the video coding standards, it consumes a lot of memory resources and time processing due to its superior video quality and coding performance. The computation algorithm of rate distortion optimisation is one of the reasons behind the encoder's bottleneck. Therefore, a new technique has been introduced here based on the entropy threshold and size of coding unit, which reduces the coding time and bit rate to be more suitable for real-time processing. According to the obtained results, the encoding time has been enhanced by 56.596% with a reduction in the bit rate reached to 74% on average.

Hardware design of a scalable and fast 2-D hadamard transform for HEVC video encoder

<span>This paper presents the hardware design of a 2-dimensional Hadamard transform used the in the rate distortion optimization module in state-of-the-art HEVC video encoder. The transform is mainly used to quickly determine optimum block size for encoding part of a video frame. The proposed design is both scalable and fast by 1) implementing a unified architecture for sizes 4x4 to 32x32, and 2) pipelining and feed through control that allows high performance for all block sizes. The design starts with high-level algorithmic loop unrolling optimization to determine suitable level of parallelism. Based on this, a suitable hardware architecture is devised using transpose memory buffer as pipeline memory for maximum performance. The design is synthesized and implemented on Xilinx Kintex Ultrascale FPGA. Results indicate variable performance obtained for different block sizes and higher operating frequency compared to a similar work in literature. The proposed design can be used as a hardware accelerator to speed up the rate distortion optimization operation in HEVC video encoders.</span>

SSIM-Based Global Optimization for CTU-Level Rate Control in HEVC

In this paper, we propose a coding tree unit (CTU)-level rate control scheme from the perspective of SSIM-based rate-distortion optimization to improve the coding efficiency. First, we establish the SSIM-based rate-distortion model based on the divisive normalization scheme, which characterizes the relationship between the local visual quality and the coding bits. Then, the established model is applied to the CTU-level rate control and transformed into a global optimization problem solved by convex optimization. Finally, a new model parameter updating strategy for the CTU-level rate control is presented that is robust to scene variations. Our algorithm can achieve optimal CTU-level bit allocation given the bit-rate budget. The experimental results show that our algorithm substantially enhances the coding performance and consistently outperforms both the rate control scheme in the HEVC reference software and existing algorithms in terms of rate-perceptual distortion performance using different test configurations.

Distortion propagation modeling and its applications on frame level quantization control for predictive video coding

Inter-frame temporal prediction in video coding causes distortion propagation among adjacent frames. As a result, frame level bit allocation and quantization control are intrinsically to be optimized with dependent rate distortion optimization (RDO). Taking inter-frame distortion propagation into consideration, quantization parameter cascading (QPC) is an efficient technique for frame level quantization control in terms of dependent RDO. This paper proposes a temporal distortion propagation model by quantitatively evaluating the temporal distortion dependency. Block-level temporal trajectory is tracked via inter-prediction on down-sampled original frames to construct the temporal analysis chain, and then tree-style dependent analysis is implemented along the trajectory. The amounts of equivalent distortion propagated from the temporally adjacent frames are quantitatively measured. Then, a new distortion model is developed to facilitate measuring the RD (rate distortion) cost in terms of dependent RDO. Finally, a simplified trellis comprised of candidate quantization parameters (Qp) of the frames within one GOP, and the optimal Qp is searched via simplified dynamic programming to achieve global optimization. The simulation results verify that the frame level QPC algorithm achieves 3.46% BD-RATE saving on average, which is contributed by efficient bit allocation along temporal trajectory.

Statistical Early Termination and Early Skip Models for Fast Mode Decision in HEVC INTRA Coding

In this article, statistical Early Termination (ET) and Early Skip (ES) models are proposed for fast Coding Unit (CU) and prediction mode decision in HEVC INTRA coding, in which three categories of ET and ES sub-algorithms are included. First, the CU ranges of the current CU are recursively predicted based on the texture and CU depth of the spatial neighboring CUs. Second, the statistical model based ET and ES schemes are proposed and applied to optimize the CU and INTRA prediction mode decision, in which the coding complexities over different decision layers are jointly minimized subject to acceptable rate-distortion degradation. Third, the mode correlations among the INTRA prediction modes are exploited to early terminate the full rate-distortion optimization in each CU decision layer. Extensive experiments are performed to evaluate the coding performance of each sub-algorithm and the overall algorithm. Experimental results reveal that the overall proposed algorithm can achieve 45.47% to 74.77%, and 58.09% on average complexity reduction, while the overall Bjøntegaard delta bit rate increase and Bjøntegaard delta peak signal-to-noise ratio degradation are 2.29% and −0.11 dB, respectively.

Fast H.264 to HEVC Transcoding: A Deep Learning Method

With the development of video coding technology, high-efficiency video coding (HEVC) has become a promising alternative, compared with the previous coding standards, for example, H.264. In general, H.264 to HEVC transcoding can be accomplished by fully H.264 decoding and fully HEVC encoding, which suffers from considerable time consumption on the brute-force search of the HEVC coding tree unit (CTU) partition for rate-distortion optimization (RDO). In this paper, we propose a deep learning method to predict the HEVC CTU partition, instead of the brute-force RDO search, for H.264 to HEVC transcoding. First, we build a large-scale H.264 to HEVC transcoding database. Second, we investigate the correlation between the HEVC CTU partition and H.264 features, and analyze both temporal and spatial-temporal similarities of the CTU partition across video frames. Third, we propose a deep learning architecture of a hierarchical long short-term memory (H-LSTM) network to predict the CTU partition of HEVC. Then, the brute-force RDO search of the CTU partition is replaced by the H-LSTM prediction such that the computational time can be significantly reduced for fast H.264 to HEVC transcoding. Finally, the experimental results verify that the proposed H-LSTM method can achieve a better tradeoff between coding efficiency and complexity, compared to the state-of-the-art H.264 to HEVC transcoding methods.

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.

Enhanced Intra Prediction Based on Adaptive Coding Order and Multiple Reference Sets in HEVC

High Efficiency Video Coding (HEVC) is the most recent video coding standard. It can achieve a significantly higher coding performance than previous video coding standards, such as MPEG-2, MPEG-4, and H.264/AVC (Advanced Video Coding). In particular, to obtain high coding efficiency in intra frames, HEVC investigates various directional spatial prediction modes and then selects the best prediction mode based on rate-distortion optimization. For further improvement of coding performance, this paper proposes an enhanced intra prediction method based on adaptive coding order and multiple reference sets. The adaptive coding order determines the best coding order for each block, and the multiple reference sets enable the block to be predicted from various reference samples. Experimental results demonstrate that the proposed method achieves better intra coding performance than the conventional method.

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.

Fast 3D-HEVC PU size decision algorithm for depth map intra-video coding

High-Efficiency Video Coding (HEVC)-based 3D video coding (3D-HEVC) is the most recent standard and last exertion of ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) for 3D video coding using a new data video format called Multi-View Video plus Depth map (MVD). This new standard achieves a high coding improvement. In any case, one of the most critical difficulties in 3D-HEVC is time computational complexity. The depth map intra-prediction is a critical factor in 3D-HEVC intra-coding, in which, the 3D-HEVC uses a highly adaptable Coding Unit (CU) structure with a specific end goal to expand the coding efficiency of all depth map characteristics. However, it results in an enormous Rate Distortion Optimization Cost (RDO-Cost) because of the broad recursive search for the best CU size from $$64\times 64$$ down to $$4\times 4$$ . This computational complexity excludes the 3D-HEVC from true and real-time application. Hence, it is imperative to build up an algorithm to diminish the complexity of the size decision in depth map intra-coding. To determine the previously mentioned issue, this paper proposes an effective 3D-HEVC PU size decision algorithm for depth map intra-video coding based on tensor features and statistical data analyses. The experimental results demonstrate that the proposed model diminishes the complexity of depth map size decision significantly with low rate distortion increase.

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

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.

Occlusion-Aware Depth Map Coding Optimization Using Allowable Depth Map Distortions

In depth map coding, rate-distortion optimization for those pixels that will cause occlusion in view synthesis is a rather challenging task, since the synthesis distortion estimation is complicated by the warping competition and the occlusion order can be easily changed by the adopted optimization strategy. In this paper, an efficient depth map coding approach using allowable depth map distortions is proposed for occlusion-inducing pixels. First, we derive the range of allowable depth level change for both the zero disparity error case and non-zero disparity error case with theoretic and geometrical proofs. Then, we formulate the problem of optimally selecting the depth distortion within allowable depth distortion range with the objective to minimize the overall synthesis distortion involved in the occlusion. The unicity and occlusion order invariance properties of allowable depth distortion range is demonstrated. Finally, we propose a dynamic programming based algorithm to locate the optimal depth distortion for each pixel. Simulation results illustrate the performance improvement of the proposed algorithm over the other state-of-the-art depth map coding optimization schemes.

Spherical Domain Rate-Distortion Optimization for Omnidirectional Video Coding

Efficient compression of omnidirectional video is important for emerging virtual reality applications. To compress this kind of video, each frame is projected to a 2D plane [e.g., equirectangular projection (ERP) map] first, adapting to the input format of existing video coding systems. At the display side, an inverse projection is applied to the reconstructed video to restore signals in spherical domain. Such a projection, however, makes presentation and encoding in different domains. Thus, an encoder agnostic to the projection performs inefficiently. In this paper, we analyze how a projection influences the distortion measurements in different domains. Based on the analysis, we propose a scheme to optimize the encoding process based on signals’ distortion in spherical domain. With the proposed optimization, an average 4.31% (up to 9.67%) luma BD-rate reduction is achieved for ERP in random access configuration. The corresponding bit saving is averagely 10.84% (up to 34.44%) when considering the viewing field being $\pi /2$ . The proposed method also benefits other projections and viewport settings, with a marginal complexity increase.

Early Termination of Intra Mode Decision Based on Most Probable Mode and SATD for H.264/AVC encoding

Intra coding in H.264/AVC can significantly improve the coding efficiency but at the cost of high computational complexity due to the use of rich prediction modes and rate-distortion optimization technique. In this paper, an early termination algorithm of intra mode decision is presented to address the complexity issue in 4×4 intra prediction. The proposed algorithm is motivated by two facts: the most probable mode defined based on the spatial similarity has a high possibility to be the best mode, and a good prediction usually has a small residual block which is measured by the sum of absolute transformed difference (SATD). The most probable mode and two modes with the smallest SATD values are investigated to select the candidate modes by using the proposed early termination rules. Experimental results show that the proposed algorithm effectively reduces the complexity of 4×4 intra prediction while maintaining almost the same coding performance on peak signal-to-noise ratio and bit rate compared with the full search algorithm.

Multiple Histograms-Based Reversible Data Hiding: Framework and Realization

Reversible data hiding (RDH) has unique advantage in copyright and integrity protection for multimedia contents. As a typical RDH scheme, histogram shifting technique (HS) has found wide applications due to its high quality of marked image. At present, most existing HS-based RDH schemes rely on single histogram generated from cover image to hide data. Since the single histogram-based approach (SH_RDH) commonly employs smooth regions in the cover image for data hiding, it might not well utilize the cover image and exploit the correlations among image contents of different texture characteristics. In this paper, a novel RDH general framework using multiple histograms modification (MH_RDH) is proposed, which involves two key issues as follows: 1) the construction of multiple histograms based on optimized multi-features and 2) the rate allocation among multiple histograms is formulated as the one of rate-distortion optimization and solved with evolutionary algorithms. The experimental results show that the proposed method could considerably increase the payload of current MH_RDH-based embedding (ranging from 0.2 to 0.7 bpp for most test images) and outperform the other state-of-the-art SH_RDH and MH_RDH schemes.

High-Quality Color Image Compression by Quantization Crossing Color Spaces

Coding of a color image usually happens in the YCbCr space so that the rate-distortion optimization is conducted in this space. Due to the use of a non-unitary matrix in the RGB-to-YCbCr conversion, an optimal coding performance achieved in the YCbCr space does not guarantee an optimal quality in the RGB space, which would impact most display devices that need RGB signals as the inputs. In this paper, we first study the relationship between the coding distortions of the compressed RGB signals and the quantization errors occurred in the coded YCbCr signals. Then, we design a new quantization scheme crossing the RGB and YCbCr spaces to achieve a high-quality color image compression with the YCbCr 4:4:4 format. Although our proposed quantization takes place in the YCbCr space, it aims at reducing the coding distortion in the RGB space as much as possible. Experimental results demonstrate that our proposed method offers a significant quality gain over the existing block-based coding methods for various images.

Computation-Performance Optimization of Convolutional Neural Networks With Redundant Filter Removal

Convolutional neural networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many filters to extract the knowledge behind it. However, while the depth of convolutional layers gets deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, inspired by rate-distortion optimization in image and video coding, we propose a computation-performance optimization (CPO) method to remove the redundant convolution filters in a CNN with performance constraints. To prove the effectiveness of the proposed method, CPO is applied to the networks for image super-resolution and image classification. Under almost the same PSNR drop and accuracy drop for performance evaluation in these two tasks, we can achieve the best parameter and computation reduction when compared with previous works.

High-Definition Video Compression System Based on Perception Guidance of Salient Information of a Convolutional Neural Network and HEVC Compression Domain

The new generation of the high-efficiency video coding (HEVC) video coding standard has improved compression performance and brought considerable coding complexity. Therefore, reducing the perception redundancy of video to obtain a better compression effect is the new direction of video development at present. In this paper, the HEVC is improved and enhanced from the aspects of a video saliency algorithm based on an attention mechanism and a video compression algorithm based on perception priority. In terms of video saliency, this paper proposes a spatial saliency algorithm based on a convolutional neural network and a temporal saliency algorithm based on motion vector. The saliency algorithm can combine the motion estimation results of each block during the HEVC compression on the basis of convolutional neural network and carry out adaptive dynamic fusion of the two, to complete the saliency map of the input video. In the aspect of the video compression algorithm with perception priority, this paper proposes a more flexible QP selection method, which selects its corresponding QP according to the saliency value of CU. At the same time, we propose a new rate-distortion optimization algorithm, which integrates the current block’s saliency feature into the traditional rate-distortion calculation method, to guide the allocation of bits and achieve the purpose of perception priority. The experimental results proved the superiority of the proposed method over the state-of-the-art perceptual coding algorithms in terms of saliency detection and perceptual compression quality.

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.