Rate-distortion Model Research Articles

Rate distortion model based on dual parameters Weibull distribution

According to analysis of discrete cosine transform （DCT） coefficients of video sequence, a new DCT coefficients statistical model based on Weibull probability density function（PDF） is introduced. Compared with Laplacian PDF and Cauchy PDF, the model can describe properties of DCT of video sequences much better. In addition, according to the new statistical model and theory of entropy coding, the rate quantization （RQ） and distortion quantization （DQ） functions are proposed separately. Based on the simplified and approximate RQ and DQ which are obtained by the theory of rate distortion （RD） model and the properties of real video sequence, the novel RD model is presented. The experimental results show that the real video sequence encoded by intra or inter can be described much better by using rate distortion model based on Weibull PDF.

Optimal bit allocation and efficient rate control for H.264/AVC based on general rate-distortion model and enhanced coding complexity measure

A general rate-distortion (RD) model for block-based video coding is proposed in this study. Based on this model, an innovative coding characteristic called texture-complexity (TC), which is defined as a function of discrete cosine transform (DCT) coefficient distribution and distortion-quantisation (DQ) relationship as well as variance of residual block, is presented for rate control. Moreover, an adaptive linear TC forward-prediction model is established to estimate the characteristic of incoming image block, and a head bits prediction model as well as an optimal bits allocation method are also proposed to best allocate available bits. Experimental results show that the H.264 encoder (JM15.1), using the proposed rate control algorithm, achieves an objective quality improvement up to 0.434 dB, produces flatter bit-rate/PSNR curve and meets closer target bits budget than outcomes of JM15.1 rate control method (JVT-W042).

Optimal bit allocation for subband video coding

An optimal subband bit allocation technique for optimal distribution of bits among the subbands is presented. The proposed technique is based on a new rate-distortion model, which switches between two types of models at lower and higher bit rates. Based on this model, it is possible to select the best R-D strategy among the lower bit rate model, the higher bit rate model and the average between them. The Lagrangian optimisation method is then used to minimise this distortion model for a given bit budget and to obtain an optimum bit distribution among the subbands. The proposed optimal bit allocation framework is implemented on the motion-compensated 2D subband/DCT scalable video codec, which provides quality signal-to-noise ratio (SNR) scalability. The results show that the proposed bit allocation method outperforms the uniform allocation of bits over a wide range of target bit rates. Moreover, simulation results show that the performance of the presented scalable video coding is slightly inferior to the single layer H.264/AVC but outperforms the SNR scalability supported in joint scalable video model (JSVM), the state-of-the-art scalable video coding standard, that achieves its gain through the hierarchical B-pictures.

Error Resilient Video Transmission over Wireless Networks Based on Wyner-Ziv Coding of Motion Regions

The multipath fading and shading of the wireless networks usually lead to the loss or error of video packets which results in significant video quality degradation. Existing approaches with forward error correction (FEC) or error concealment are unable to provide the desired robustness in video transmission. In this work, we develop a novel motion-based Wyner-Ziv coding (MWZC) scheme by leveraging distributed source coding (DSC) ideas for error robustness. The MWZC scheme is based on the fact that motion regions of a given video frame are particularly important to both objective and perceptual video quality and hence should be given preferential Wyner-Ziv coding based embedded protection. To achieve high coding efficiency, we determine the underlining motion regions based on a rate-distortion model. Within the framework of H.264/AVC specification, motion region determination can be efficiently implemented using Flexible Macroblock Ordering (FMO) and Data Partitioning (DP). The bit stream consists of two parts: the systematic portion generated from conventional H.264/AVC bit stream and the supplementary bit stream generated by the proposed feedback free rate allocation algorithm for Wyner-Ziv coding of motion regions. Experimental results demonstrate that the proposed scheme significantly outperforms both decoder-based error concealment (DBEC) and conventional FEC with DBEC approaches.

A Rate Control Algorithm for Scalable Video Coding

The joint scalable video model(JSVM)as a scalable extension of H.264/AVC provides a high efficient solution for scalable video coding(SVC).However,there is not efficient rate control scheme is specified.This paper presents a new rate control scheme for the JSVM based on the special structure of hierarchical B decomposition(or motion compensation temporal filtering(MCTF)).In a sense of bit allocation,a novel hierarchical bit allocation strategy is proposed.Meanwhile,the different rate-distortion(R-D)model is provided for each kind picture with different picture type or temporal decomposition level.Experiment results show that the mismatch of target bit rate and real bit rate generated for coding doesn't exceed 2%;meanwhile,the PSNR improvement can be up to 1dB in low bit rate;furthermore,comparing to the original rate control method which achieves the target bit rate through regulating quantization parameter(QP)step by step,the computation complexity of the proposed rate control algorithm base on experimental model is reduced drastically.

Rate Distortion Analysis and Bit Allocation Scheme for Wavelet Lifting-Based Multiview Image Coding

This paper studies the distortion and the model-based bit allocation scheme of wavelet lifting-based multiview image coding. Redundancies among image views are removed by disparity-compensated wavelet lifting (DCWL). The distortion prediction of the low-pass and high-pass subbands of each image view from the DCWL process is analyzed. The derived distortion is used with different rate distortion models in the bit allocation of multiview images. Rate distortion models including power model, exponential model, and the proposed combining the power and exponential models are studied. The proposed rate distortion model exploits the accuracy of both power and exponential models in a wide range of target bit rates. Then, low-pass and high-pass subbands are compressed by SPIHT (Set Partitioning in Hierarchical Trees) with a bit allocation solution. We verify the derived distortion and the bit allocation with several sets of multiview images. The results show that the bit allocation solution based on the derived distortion and our bit allocation scheme provide closer results to those of the exhaustive search method in both allocated bits and peak-signal-to-noise ratio (PSNR). It also outperforms the uniform bit allocation and uniform bit allocation with normalized energy in the order of 1.7-2 and 0.3-1.4 dB, respectively.

Content-Aware Distortion-Fair Video Streaming in Congested Networks

Internet is experiencing a substantial growth of video traffic. Given the limited network bandwidth resources, how to provide Internet users with good video playback quality-of-service (QoS) is a key problem. For video clips competing bandwidth, we propose an approach of Content-Aware distortion-Fair (CAF) video delivery scheme, which is aware of the characteristics of video frames and ensures max-min distortion-fair sharing among video flows. CAF leverages content-awareness to prioritize packet dropping during congestion. Different from bandwidth fair sharing, CAF targets end-to-end video playback quality fairness among users. The proposed CAF approach does not require rate-distortion modeling of the source, which is difficult to estimate. Instead, it exploits the temporal prediction structure of the video sequences along with a frame drop distortion metric to guide resource allocations and coordinations. Experimental results show that the proposed approach operates with limited overhead in computation and communication, and yields better QoS, especially when the network is congested.

Lagrange multiplier selection in wavelet-based scalable video coding for quality scalability

In this paper, a method for Lagrange multiplier selection is proposed in the context of rate-distortion optimisation for wavelet-based scalable video coding targeting quality scalability. Despite the prevalence of the conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to wavelet-based scalable video coding. To address the inherent challenges, a thorough analysis of the rate-distortion models for transform video coding is provided with regard to low and middle-to-high bit-rates, respectively. Based on the analysis, the models are consolidated according to experimental observations and the consolidated rate-distortion models serve as the basis for the derivation of the Lagrange multiplier. Considering the influence of the open-loop prediction structure on the rate-distortion performance, the Lagrange multiplier is initially derived for a single-targeted bit-rate. Moreover, the method for Lagrange multiplier selection in scalable video coding aiming at multiple-targeted bit-rates is proposed in a general sense of bit-rate range, varying from low to high bit-rates, building on the initially derived Lagrange multiplier for a single-targeted bit-rate. The proposed Lagrange multiplier is content adaptive and well suited for wavelet-based scalable video coding where quantisation steps are unavailable. Detailed performance evaluation of the proposed method for wavelet-based scalable video coding is provided with regard to a given targeted bit-rate and multiple-targeted bit-rates, respectively. The experimental results have demonstrated the effectiveness of the proposed Lagrange multiplier for rate-distortion optimisation considering quality scalability in wavelet-based scalable video coding.

Convex Optimization-Based Bit Allocation for Video Coding

This paper presents a new convex optimization-based bit allocation framework for video coding. Motivated by the statistical analysis of the relationship between the rate and the corresponding distortion in typical video sequences, an improved empirical rate-distortion model with more flexibility in explaining the experimental observations is proposed. The convexity and monotonicity properties of the proposed model allow us to formulate the bit allocation problem as a convex programming problem, which can be solved using interior-point methods. Coupled with the region of interest (ROI) functionality and the technique of MB classification, the proposed bit allocation scheme is then applied to the problem of frame-level bit allocation for various video coding standards employing motion compensated hybrid DCT/DPCM technique. Similar bit allocation scheme is also developed for MPEG-4 video object coding at the object level. The relevant model parameters are determined using previously encoded data by means of linear regression. Simulation results show that the proposed algorithms can achieve considerably better PSNR performance than the conventional approaches for the tested video sequences, at the expenses of slight increase in coding complexity.

Efficient rate control scheme for low bit rate H.264/AVC video coding

This article presents an efficient rate control scheme for H.264/AVC video coding in low bit rate environment. In the proposed scheme, an improved rate-distortion (RD) model by both analytical and empirical approaches is developed. It involves an enhanced mean absolute difference estimating method and a more rate-robust distortion model. Based on this RD model, an efficient macroblock-layer rate control scheme for H.264/AVC video coding is proposed. Experimental results show that this model encodes video sequences with higher peak signal-to-noise ratio gains and generates bit stream closer to the target rate.

New rate-distortion modeling and efficient rate control for H.264/AVC video coding

Rate control (RC) is crucial in controlling compression bit rates and qualities for networked video applications. In this paper, we propose a new rate-distortion (R-D) model and an efficient rate control scheme for H.264/AVC video coding, which elegantly resolve the inter-dependency problem between rate-distortion optimization and rate control by eliminating the need of coding complexity prediction for an inter-frame. The objective is to achieve accurate bit rate, obtain optimal video quality while reducing quality variations and simultaneously handling buffer fullness effectively. The proposed algorithm encapsulates a number of new features, including a coding complexity measure for intra-frames, a rate-distortion model, an accurate quantization parameter (QP) estimation for intra-frames, an incremental quantization parameter calculation method for inter-frames, a proportional+integral+derivative (PID) buffer controller, and an intelligent bit-allocation-balancing technique. Our experimental results demonstrate that the proposed scheme outperforms the JVT-G012 solution by providing accurate rate regulation, effectively reducing frame skipping, and finally improving coding quality by up to 1.80 dB.

Generalized statistics framework for rate distortion theory

Variational principles for the rate distortion (RD) theory in lossy compression are formulated within the ambit of the generalized nonextensive statistics of Tsallis, for values of the nonextensivity parameter satisfying 0 < q < 1 and q > 1 . Alternating minimization numerical schemes to evaluate the nonextensive RD function, are derived. Numerical simulations demonstrate the efficacy of generalized statistics RD models.

Analysis and Application on Rate-Distortion Model Oriented Scalable Video Sequences

Analysis and Application on Rate-Distortion Model Oriented Scalable Video Sequences

Frame layer rate control for H.264/AVC with hierarchical B-frames

Hierarchical B-frames contribute to improvement of coding performance when introduced into H.264/AVC. However, the existing rate control schemes for H.264/AVC, which are mainly applied to IPPP and IBBP coding structures, cannot work efficiently for the coding structures with hierarchical B-frames. In this paper, a frame layer rate control scheme for hierarchical B-frames is proposed. Firstly, an adaptive starting quantization parameter (QP) determination method is implemented to derive the QP for the first coding frame based on the available channel bit rate and the content of the current video sequence. Then, the target bit budget for a group of pictures (GOP) is calculated based on the target bit rate and the buffer status. Afterwards, a temporal level (TL) layer rate control phase is introduced, and the GOP layer target bit budget is allocated to each TL. In the frame layer rate control phase, a method based on a rate-distortion model and the coding properties of the previous coded key frames is derived to determine the QP for the current key frame. For hierarchical B-frames, we introduce a typical weighting factor in the determination of their target bit budgets to address the features of the hierarchical coding structures. This weighting factor is calculated according to the target bit budget of the GOP layer and the knowledge obtained from the previous coded B-frames in each TL. Subsequently, the QP for coding the current B-frame is computed by a quadratic model with different model parameters for different TLs, and the computed QP is further adaptively adjusted according to the usage of the target bit budgets. After coding the current frame, an update stage, in which a threshold-based method is integrated to avoid model degradation, is invoked to update the parameters for rate control. Experimental results demonstrate that when the proposed rate control scheme is applied to the coding structure with hierarchical B-frames in H.264/AVC, the actual coding bit rates can match the target bit rates very well, and the encoding performance is also improved.

A Rate Control Scheme of the Even Low Bit-Rate Video Encoder

Rate control plays an important role in transmitting low-delay and high-quality images over the channel of very low bandwidth. The rate control algorithm in MPEG-4 or H.26X only defined the rate control model of P-frame, and did not introduce the rate control model of I-frame as it supposed that only the first frame is an I-frame, the others are all P-frames. However, in practical applications, a certain number of I-frames have to be inserted to meet the demand for fault-tolerant transmission and information retrieval. Oriented toward the application demand of remote surveillance system based on PSTN, a rate control strategy is proposed for encoding video at even low bit-rate in this paper. The strategy includes a rate-control algorithm for I-frame and introduces a quadratic rate distortion model, which solves the problem of the rate control used at very low bit-rate preferably. The rate distortion model of I-frame is designed referring to that of P-frame. It mainly involves three processes: initial quantization parameter calculation, objective bit number allocation, and objective quantization parameter calculation. TM5 of MPEG-2 proposed the target bit-number allocation model of I, P, and B frame, but the algorithm for the model is a bit complex. Based on the quadratic rate distortion model, a novel algorithm for the target bit-number allocation is presented in this paper. In order to keep the stability of image quality, VM8 limits the change range of the new quantization parameter from −0.25 to 0.25 times of the previous frame. As a result, if the previous frame's quantization parameter is small but the current frame's quantization parameter is calculated to be large, the current frame's quantization parameter should have been corrected with a small value, which will cause excessive coding bits, and worsen the transmission delay. In this paper, we present a correction algorithm for the computation of P-frame's quantization parameter. The results of experiments show that the proposed scheme reduces the delay of encoder buffering greatly, and improves the quality of reconstructed images clearly at the same time. In terms of the system's transmission delay, our scheme uses reasonable algorithms for computing the quantization parameters of I-frame and P-frame, and adjusting the GOP length dynamically. The transmission delays are significantly decreased and the buffer overflows are avoided. In terms of the reconstructed image quality, the test sequence's PSNRs with our scheme are higher than VM8's at all the bit rates, and the coded bit-number of our scheme is less than VM8's at the same bit rate. In terms of the deviation of output bit-streams, the average deviation of our algorithm is much less than VM8's. The less the deviation of the output bit-stream is, the more appropriate it is to be transmitted at the given bandwidth, and the less the transmission delay is.

Novel Rate-Control Algorithm Based on TM5 Framework

Rate control is a key technology in the fields of video coding and transmission, and it has attracted a great attention and has been studied extensively. The TM5 framework of MPEG-2 is a classical rate control algo-rithm and has being widely used. However, it has some underlying drawbacks during practical applications such as the poor rate control precision and high computational complexity. Hence, in this paper, a novel rate-control algorithm based on the TM5 framework is proposed. The drawback of the target bit allocation method of the original TM5 algorithm is firstly analyzed and improved. Then, a new rate-distortion model is incorporated into the rate control algorithm to implement rate prediction to enhance the rate-control precision. Meanwhile, the macro-block (MB) level rate control is adapted to be frame level to reduce the computational complexity. Experiments are conducted and some results are given. Compared with the original TM5 algo-rithm, the improved novel algorithm not only can enhance the rate-control precision but also can reduce the complexity and the fluctuation of decoded image quality.

Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization

This paper presents a new lossy coding scheme based on 3D wavelet transform and lattice vector quantization for volumetric medical images. The main contribution of this work is the design of a new codebook enclosing a multidimensional dead zone during the quantization step which enables to better account correlations between neighbor voxels. Furthermore, we present an efficient rate–distortion model to simplify the bit allocation procedure for our intra-band scheme. Our algorithm has been evaluated on several CT- and MR-image volumes. At high compression ratios, we show that it can outperform the best existing methods in terms of rate–distortion trade-off. In addition, our method better preserves details and produces thus reconstructed images less blurred than the well-known 3D SPIHT algorithm which stands as a reference.

On Channel Capacity Estimation and Prediction for Rate-Adaptive Wireless Video

Packet drops caused by residue errors (MAC-layer errors) can severely deteriorate the wireless video quality. Prior studies have shown that this loss of quality can be circumvented by using forward error correction (FEC) to recover information from the corrupted packets. The performance of FEC encoded video streaming is critically dependent upon the choice of source and channel coding rates. In practice, the wireless channel conditions can vary significantly, thus altering the optimal rate choices. Thus, it is essential to develop an architecture which can estimate the channel capacity and utilize this estimate for rate allocation. In this paper we develop such a framework. Our contributions consist of two parts. In the first part we develop a prediction framework that leverages the received packets' signal to silence ratio (SSR) indications and MAC-layer checksum as side information to predict the operational channel capacity. In the second part, we use this prediction framework for rate allocation. The optimal rate allocation is dependent upon the channel capacity, the distribution of the (capacity) prediction error and the rate-distortion (RD) characteristics of the video source. Consequently, we propose a framework that utilizes the aforementioned statistics for RD optimal rate adaptation. We exhibit the efficacy of the proposed scheme by simulations using actual 802.11b wireless traces, an RD model for the video source and an ideal FEC model. Simulations using source RD models derived from five different popular video codecs (including H.264), show that the proposed framework provides up-to 5-dB improvements in peak signal-to-noise ratio (PSNR) when compared with conventional rate-adaptive schemes.

On the accuracy and complexity of rate-distortion models for fine-grained scalable video sequences

Rate-distortion (R-D) models are functions that describe the relationship between the bitrate and expected level of distortion in the reconstructed video stream. R-D models enable optimization of the received video quality in different network conditions. Several R-D models have been proposed for the increasingly popular fine-grained scalable video sequences. However, the models' relative performance has not been thoroughly analyzed. Moreover, the time complexity of each model is not known, nor is the range of bitrates in which the model produces valid results. This lack of quantitative performance analysis makes it difficult to select the model that best suits a target streaming system. In this article, we classify, analyze, and rigorously evaluate all R-D models proposed for FGS coders in the literature. We classify R-D models into three categories: analytic, empirical, and semi-analytic. We describe the characteristics of each category. We analyze the R-D models by following their mathematical derivations, scrutinizing the assumptions made, and explaining when the assumptions fail and why. In addition, we implement all R-D models, a total of eight, and evaluate them using a diverse set of video sequences. In our evaluation, we consider various source characteristics, diverse channel conditions, different encoding/decoding parameters, different frame types, and several performance metrics including accuracy, range of applicability, and time complexity of each model. We also present clear systematic ways (pseudo codes) for constructing various R-D models from a given video sequence. Based on our experimental results, we present a justified list of recommendations on selecting the best R-D models for video-on-demand, video conferencing, real-time, and peer-to-peer streaming systems.

Rate-distortion optimized streaming of fine-grained scalable video sequences

We present optimal schemes for allocating bits of fine-grained scalable video sequences among multiple senders streaming to a single receiver. This allocation problem is critical in optimizing the perceived quality in peer-to-peer and distributed multi-server streaming environments. Senders in such environments are heterogeneous in their outgoing bandwidth and they hold different portions of the video stream. We first formulate and optimally solve the problem for individual frames, then we generalize to the multiple frame case. Specifically, we formulate the allocation problem as an optimization problem, which is nonlinear in general. We use rate-distortion models in the formulation to achieve the minimum distortion in the rendered video, constrained by the outgoing bandwidth of senders, availability of video data at senders, and incoming bandwidth of receiver. We show how the adopted rate-distortion models transform the nonlinear problem to an integer linear programming (ILP) problem. We then design a simple rounding scheme that transforms the ILP problem to a linear programming (LP) one, which can be solved efficiently using common optimization techniques such as the Simplex method. We prove that our rounding scheme always produces a feasible solution, and the solution is within a negligible margin from the optimal solution. We also propose a new algorithm (FGSAssign) for the single-frame allocation problem that runs in O ( n log n ) steps, where n is the number of senders. We prove that FGSAssign is optimal. Furthermore, we propose a heuristic algorithm (mFGSAssign) that produces near-optimal solutions for the multiple-frame case, and runs an order of magnitude faster than the optimal one. Because of its short running time, mFGSAssign can be used in real time. Our experimental study validates our analytical analysis and shows the effectiveness of our allocation algorithms in improving the video quality.