Rate-distortion Optimized Quantization Research Articles

Enhancing HEVC Compression: A Novel Algorithm for Swift and Efficient Intra Prediction

High-Efficiency Video Coding (HEVC) represents the cutting-edge in video compression standards for high-resolution visual data. The prowess of HEVC lies in its utilization of prediction, capitalizing on signal redundancies to attain optimal compression efficiency. A key feature, the Intra-Picture prediction block, anticipates a block within the present frame by referencing information from adjacent blocks within that same frame. Nonetheless, this innovation introduces a significant surge in encoding complexity. Addressing this surge and reducing the computational demands of the coder becomes imperative. The present research introduces an innovative algorithm targeting mode decisions grounded on intra prediction. This algorithm assesses the texture intricacy of the coding unit and establishes an optimal threshold, enabling swift coding unit size selection. Subsequently, an enhanced search procedure minimizes the candidate modes of prediction. Culminating this approach, early termination gets implemented for rate-distortion optimized quantization (RDOQ) within Intra prediction. Findings indicate that the novel algorithm facilitates a reduction of approximately 40.9% in encoding duration.

Rate-distortion optimized quantization for geometry-based point cloud compression

Limited by the network bandwidth, three-dimensional (3D) point cloud needs to be efficiently compressed before transmission. As one of the three attribute coding methods adopted in the geometry-based point cloud compression (G-PCC) standard developed by MPEG, predicting transform (PT) has received increasing attention. To further improve the coding efficiency of PT, we propose a rate-distortion optimized quantization (RDOQ) in which an additional option for quantization results is added by setting the quantized residuals to zero forcedly. Rate-distortion optimization is then used to determine whether the final quantized residual should be zero or nonzero. Experimental results show that average BD rates of −0.5 % , −3.0 % , and −3.0 % can be achieved for Luma, Chroma Cb, and Chroma Cr components, respectively, with negligible increment of time complexity.

Implicitly Selected Transform for AVS3.

Transform coding removes redundancy by de-correlating the residual data, which plays a crucial role in video coding. Since different transform cores can adapt to different video content and residual data, multiple cores transform has been proposed to improve the coding performance. However, the overhead for representing the transform type is inevitable. This paper intensively studies the statistical characteristics of transform coefficients. Theoretical analysis shows that the implicit method in the Implicitly Selected Transform (IST) is superior to the explicit signalling method. As such, we propose the parity adjustment scheme which seamlessly cooperates with the rate-distortion optimization quantization for IST. Furthermore, we propose two combination methods, size-based and number-based, to optimize IST. Moreover, a restriction region of IST is applied to reduce the complexity. Experimental results on HPM-6.0, which is the reference software of the AVS3 video coding standard, show that our proposed method can achieve 1.76% and 0.76% BD-Rate savings on average under AI and RA configurations, respectively, along with negligible decoding time variations. The comparison results between the proposed method and explicit signalling method illustrate that the proposed method can achieve better coding gain than the latter. Our method has been adopted in AVS3.

Multi-stage all-zero block detection for HEVC coding using machine learning

Compared with deadzone hard-decision quantization (HDQ), rate-distortion optimized quantization (RDOQ) in HEVC brings non-negligible coding gain, however consumes considerable computations caused by exhaustive search over multiple candidates to determine optimal output level. Benefiting from efficient prediction in HEVC, transform blocks are frequently quantized to all zero, especially in small-size blocks. It is worthwhile to detect all zero block (AZB) for transform blocks to bypass subsequent computation-intensive RDOQ. Traditional thresholding based AZB detection algorithms are well-suited for deadzone quantized blocks, however miss partial optimal results in RDOQ and suffer from more or less accuracy degradation in RDOQ. This paper proposes a novel multi-stage AZB detection algorithm for RDOQ blocks with good tradeoff between complexity and accuracy. At the first stage, genuine all zero blocks (G_AZB) which are quantized to all zero both in HDQ and RDOQ are prejudged by comparison with conservative threshold determined by mathematical derivation for deadzone HDQ. At the second stage, an adaptive threshold model is built using adaptive deadzone offset by simulating the behavior patterns existing in RDOQ, aiming to further detect the pseudo AZB (P_AZB) which are quantized to all zero in RDOQ however not all zero in HDQ. At the final stage, machine learning based detection is proposed to classify the remaining “cunning” all zero blocks using eight distinguished RDO-related features, by which subtle working mechanism in RDOQ is leveraged. The experimental results demonstrate that the proposed algorithm achieves up to 7.471% total coding computation saving with 0.064% BD-RATE increment compared with RDOQ on average. Moreover, the average FNR and FPR detection accuracies are 6.3% and 6.5% respectively.

Low Complexity Quantization in High Efficiency Video Coding

The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency Video Coding (HEVC), leading to inspiring improvement in terms of rate-distortion performance. However, its heavy use imposes high complexity on the encoder in real-world video compression applications. In this paper, we provide a comprehensive review on low complexity quantization techniques in HEVC, including both fast RDOQ and all-zero block detection. In particular, the fast RDOQ relies on rate and distortion models for rate-distortion cost estimation, such that the most appropriate quantized coefficient is selected in a low complexity way. All-zero block detection infers the all-zero block by skipping transform and quantization, in an effort to further reduce the complexity. The relationship between the two techniques is also discussed, and moreover, we also envision the future design of low complexity quantization in the upcoming Versatile Video Coding (VVC) standard.

Simplified Level Estimation for Rate-Distortion Optimized Quantization of HEVC

The rate-distortion optimized quantization (RDOQ) in High Efficiency Video Coding (HEVC) brings a significant coding gain but also leads to huge computation at encoder. Since the level estimation (LE) demands dominant computational complexity among the three major internal stages inside the RDOQ, this paper addresses speeding up of the RDOQ process by simplifying the LE stage. Under the observation that in particular cases, some quantization levels are very likely to be changed (or unchanged) after the RDOQ, and zero levels do not need to go through the RDOQ process, we propose an adaptive dead-zone scheme which can make more transform coefficients of high probability to change from one to zero have zero level by quantization itself before the LE stage. Moreover, for the levels expected to keep unchanged, we introduce two approaches to simplify their decision for optimal quantized levels. Our experiment results show 14.39%, 12.88%, and 12.91% of average quantization time saving under all intra (AI-Main), random access (RA-Main), and low delay B (LB-Main) configuration, respectively. Furthermore, the average saving time for the entire encoding is 4.00% for AI-Main, 2.27% for RA-Main, and 2.33% for LB-Main, respectively. In the Bjontegaard delta bit rate (BDBR) sense, our proposed simplified level estimation framework achieves the above time reduction with merely no performance loss.

Efficient Hard-Decision Quantization Using an Adaptive Deadzone Offset Model for Video Coding

In video coding, rate distortion optimized quantization (RDOQ), a popular version of soft-decision quantization (SDQ), achieves superior coding performance, however is ill-suited for hardware implementation due to its inherent sequential processing. On the other hand, deadzone hard-decision quantization (HDQ) is friendly to hardware implementation, however suffers from non-negligible coding performance degradation. This paper proposes a content-adaptive deadzone offset model to improve the coefficient-wise deadzone HDQ by imitating the behavior patterns of RDOQ. The contributions of this paper are characterized by twofold. On one hand, this work formulates seeking optimal deadzone offset model as a problem of binary classification, and analyzes the distribution characteristics of the optimal deadzone offsets obtained from samples by fully imitating RDOQ, and then derives adaptive deadzone offset model by maximizing the right classification probability of offset-induced rounding in HDQ. On the other hand, the distribution parameters of DCT coefficients are measured in a position-wise way, and the adaptive deadzone model is built by applying Maximum a posterior estimation method according to three characteristic parameters, i.e. quantization step size, parameter of DCT coefficients, and quantization remainder, in the sense of rate distortion optimization. Simulation results verify that the proposed adaptive HDQ algorithm, in comparison with fixed-offset HDQ, achieves 0.54% and 0.52% bit rate saving on average with almost negligible complexity increment. Simultaneously, the proposed algorithm only sacrifices smaller than 0.55% and 0.54% increment in terms of BD-BR in comparison with RDOQ. The proposed HDQ is well-suited for hardwired video coding implementation.

Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization.

Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each transformed coefficient and the determination of best quantized coefficients for transform units with the minimum total cost, to softly optimize the quantized coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.

Fast Quantization Method With Simplified Rate–Distortion Optimized Quantization for an HEVC Encoder

While the rate–distortion optimized quantization (RDOQ) technique provides nontrivial coding gain in High Efficiency Video Coding (HEVC), it also involves considerable computations, whereby the complexity of quantization is significantly increased. In this paper, two schemes (the RDOQ bypass decision and the simplified level adjustment) are investigated to reduce the complexity of the quantization process in HEVC with RDOQ. The RDOQ bypass decision method initially selects the transform blocks for which the RDOQ is expected to give less/or no coding gain and enables the conventional uniform scalar quantization to be applied to these transform blocks instead of the RDOQ. The simplified level adjustment method only estimates the difference in rate–distortion costs among the candidate quantization levels to enable the encoder to select an optimal quantization level at a much reduced computational cost. Furthermore, the proposed simplified level adjustment scheme is designed so that it can be implemented in lookup tables. Experimental results show that the proposed fast method achieves 14.3% quantization complexity reduction in all intra main conditions, 15.2% in the random access main condition, and 14.9% in the low delay main condition on average with virtually no coding loss compared with the conventional quantization process with the RDOQ.

Fast Soft Decision Quantization With Adaptive Preselection and Dynamic Trellis Graph

Soft decision quantization (SDQ) is an efficient tool for video coding to achieve coefficient-level rate-distortion optimized quantization (RDOQ) with a 6%–8% bit rate saving. However, the software and hardware implementations of SDQ suffer from either high complexity or low throughput capacity due to complex Viterbi trellis search and sequential processing in context-adaptive binary arithmetic coding. In this paper, a fast SDQ algorithm is proposed to decrease the number of trellis stages to decrease the complexity and to break the data dependency in optimal SDQ. First, preselection is performed according to hard decision quantization results by intelligent coding cost estimation and comparison, during which some coefficients are judged to be safely excluded from trellis search, resulting in considerable complexity reduction. Second, a dynamic trellis graph with flexible structure is constructed according to the unsafe nonzero coefficients to accelerate the remaining partial Viterbi search. Third, a dynamic threshold selection model is proposed for adaptive thresholding to increase the probability of right preselection under a constraint on a predefined maximal probability of wrong preselection. The experimental results show that compared with optimal SDQ, the proposed algorithm can at least reduce the computation complexity by 50%−80%, memory accesses by 75%−82%, and the sequential processing latency in hardware implementation by 87.25%, with less than 0.4% Bjontegaard bit rate increment when a maximum of three unsafe coefficients are kept for trellis search in one block. This paper is suitable for high-throughput hardware and computation-sensitive software implementations for SDQ and RDOQ for H.264/Advanced Video Coding and High Efficiency Video Coding standards.

Fast Intra Mode Decision in High Efficiency Video Coding

In this paper a coding unit early termination algorithm resulting in a fast intra prediction is proposed that terminates complete full search prediction for the coding unit. This is followed by a prediction unit mode decision to find the optimal modes HEVC encoder 35 prediction modes. This includes a two-step process: firstly calculating the Sum of Absolute Differences (SAD) of all the modes by down sampling method and secondly applying a three-step search algorithm to remove unnecessary modes. This is followed by early RDOQ (Rate Distortion Optimization Quantization) termination algorithm to further reduce the encoding time. Experimental results based on several video test sequences for 30 frames from each test sequence show for HEVC a decrease of about 35%-48% in encoding, with negligible degradation in peak signal to noise ratio (PSNR). Metrics such as BD-bitrate (Bjontegaard Delta bitrate), BD-PSNR (Bjontegaard Delta Peak Signal to Noise Ratio) and RD plots (Rate Distortion) are also used.

Fast Intra Mode Decision for High Efficiency Video Coding (HEVC)

The latest High Efficiency Video Coding (HEVC) standard only requires 50% bit-rate of the H.264/AVC at the same perceptual quality, but with a significant encoder complexity increase. Hence, it is necessary and inevitable to develop fast HEVC encoding algorithms for its potential market adoption. In this paper, we propose a fast intra mode decision for the HEVC encoder. The overall fast intra mode decision algorithm consists of both micro- and macro-level schemes. At the micro-level, we propose the Hadamard cost-based progressive rough mode search (pRMS) to selectively check the potential modes instead of traversing all candidates (i.e., up to 35 in HEVC). Fewer effective candidates will be chosen by the pRMS for the subsequent rate-distortion optimized quantization (RDOQ) to derive the rate-distortion (R-D) optimal mode. An early RDOQ skip method is also introduced to further the complexity reduction. At the macrolevel, we introduce the early coding unit (CU) split termination if the estimated R-D cost [through aggregated R-D costs of (partial) sub-CUs] is already larger than the R-D cost of the current CU. On average, the proposed fast intra mode decision provides about 2.5 × speedup (without any platform or source code level optimization) with just a 1.0% Bjontegaard delta rate (BD-rate) increase using the HEVC common test condition. Moreover, our proposed solution also demonstrates the state-of-the-art performance in comparison with other works.

High-speed implementation of rate-distortion optimized quantization for H.264/AVC

Rate-distortion optimized quantization (RDOQ) is generally employed in video coding as an effective tool for pursuing high coding efficiency. Nevertheless, RDOQ requires an exhaustive search over multiple candidates to determine the optimal quantized level by comparing their rate-distortion cost, which leads to considerable complexity in practice. The utilization of inherently strong data dependency and highly sequential process in entropy coding to obtain the information of the bit-rate for each candidate further aggravates the issue of large computational burden. In this paper, a high-speed implementation of RDOQ is proposed to substitute the conventional RDOQ in H.264/AVC. The proposed scheme is low in computational complexity and economical in the requirement of hardware resources. The candidates of quantized levels that are unlikely to be selected are firstly excluded, and then a bit-rate estimation model is constructed for the rest candidates, to avoid the actual entropy coding of context-adaptive binary arithmetic coding for the calculation of the rate-distortion cost. Eventually, the fast algorithm of RDOQ is designed to determine the optimal quantized level, which supports parallel processing and is therefore favorable for hardware implementations. Experimental results demonstrate that the proposed method can decrease the time of RDOQ by 60.5 % in average with a superior RD performance in terms of encoding.

Error-Resilient Scheme for Wavelet Video Codec Using Automatic ROI Detection and Wyner-Ziv Coding Over Packet Erasure Channel

The error-resilient for video transmission over the Internet in which regarded as the packet erasure channel is always a tough task and has gained lots of attentions. The main contradictory problem lies between error-resilient and bandwidth usage. Additional redundant data has to be added to achieve robust transmission which leads to huge bandwidth usage. In this paper, an error-resilient scheme called Wyner-Ziv Error-Resilient (WZER) based on a receiver driven layered Wyner-Ziv (WZ) coding framework is proposed. The WZER purposely emphasizes on the protection of the Region of Interest (ROI) area in the frame thus to achieve the better tradeoff between the bandwidth usage and error-resilience. WZER is designed to work for the scenario of wavelet based video coding over packet erasure channel, where several techniques including automatic ROI detection, ROI mask generation, Rate distortion optimization (RDO) quantization, WZ coding with layer design, and packet level Low Density Parity Check (LDPC) code are used. The performances of the proposed WZER are simulated based on average PSNR of luminance, perceptual reconstruction and bandwidth usage and compared with normal Forward Error Correction (FEC) full protection scheme and no protection scheme. The results show the advantages of the proposed WZER over traditional FEC protection, especially in the aspects of the recovery of the subject area and bandwidth efficiency.