Adaptive rate control for High Efficiency Video Coding
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Abstract:
A frame level adaptive rate control scheme for the emerging High Efficiency Video Coding (HEVC) standard is proposed in this paper, where both rate model and distortion model are provided. For rate modeling, a new rate model is proposed based on the weighted complexity estimation of the previously encoded frames. For distortion modeling, the distortion is modeled as an exponential function of the sum of absolute transformed difference (SATD) and the quantization parameter of the current frame. Moreover, a quality smoothing method based on the distortion model is proposed to reduce the quality fluctuation. The proposed rate control algorithm is implemented into HM5.0. The proposed scheme can control the bitrate accurately with smoothing quality, and the coding gain compared with state-of-the-art technique is up to 0.64dB for LB HE & LB LC, 0.33dB, 0.31dB, 0.42dB, 0.44dB for LP HE, LP LC, RA HE and RA LC respectively.Keywords:
Smoothing
Rate distortion
Rate–distortion theory
Exponential Smoothing
Rate–distortion optimization
Algorithmic efficiency
Distortion (music)
In this paper, we propose a novel joint rate distortion optimization (JRDO) model for intra prediction coding. The spatial prediction dependency is exploited by modeling the distortion propagation with a linear fitting function. A novel JRDO based Lagrange multiplier (LM) is derived from this model. To adapt to different blocks' distortion propagation characteristics, we also introduce a generalized multiple Lagrange multiplier (MLM) framework where some candidate LMs are used in the RDO process. Experiment results show that our proposed JRDO-MLM scheme is superior to the H.264/AVC encoder.
Rate–distortion optimization
Rate distortion
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Rate-distortion cost estimation is useful for many H.264/advanced video coding (AVC) applications including rate-distortion optimization (RDO) for mode-decision and rate-control. In this paper, we propose a new rate-prediction model and an adaptive algorithm to provide more accurate estimation of the number of coding bits for encoding intra and inter-blocks compared to previously proposed methods. The rate estimation is modeled by a linear combination of existing coding parameters, which are more accurately related to entropy coding and transform coefficients. Based on the proposed model, a cost estimation function is also proposed to give a more accurate rate-distortion cost estimation. Furthermore, we propose a block classification approach to further improve the effectiveness of the proposed scheme. The proposed schemes can achieve better results in estimating the bit-rate and rate-distortion cost compared to previously proposed approaches.
Rate–distortion optimization
Rate–distortion theory
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Context-adaptive variable-length coding
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Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding [1], such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity. To resolve this problem, this paper proposes a fast TU level rate model with higher accuracy by fully imitating the behavior pattern hid in entropy.
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This paper presents a rate-distortion (RD) optimal motion estimation for depth coding. The view synthesis distortion is widely used in 3D video coding since it is a good approach to reduce the depth bit-rate without degradation of rendering quality. However, view synthesis distortion method is only applied to a mode decision process until now. The proposed method applies the view synthesis distortion to motion estimation process and achieves 0.41% bit-rate saving for AVC-compatible 3D video coding.
Rate–distortion optimization
Rate distortion
Rate–distortion theory
Distortion (music)
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We propose a rate-distortion-constrained motion estimation algorithm that leads to improvements in motion-compensated video coding. This is especially true in low bit rate video coding applications such as video conferencing. We introduce the concept of coding efficiency, and the rate-distortion optimization process is formulated as a problem of maximizing coding efficiency. Based on the coding efficiency, the proposed motion estimation algorithm measures the effect of choosing different motion vectors on the overall bit rate and reconstruction distortion. The main advantages of the algorithm are that it employs a more effective measure of performance and it is computationally simple. Our experimental results show that our proposed rate-constrained motion estimation algorithm yields better rate-distortion performance when compared with conventional motion estimation algorithms.
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This paper introduces a subband video coding algorithm for operation over a continuum of rates from very low to very high. The key elements of the system are statistical rate-distortion-constrained motion estimation and compensation, multistage residual quantization, high order statistical modeling, and arithmetic coding. The method is unique in that it provides an improved mechanism for dynamic spatial and temporal coding. Motion vectors are determined in a nontraditional way, using a rate-distortion cost criterion. This results in a smoother and more consistent motion field, relative to that produced by conventional block matching algorithms. Control over the system computational complexity and performance may be exercised easily.
Rate distortion
Rate–distortion theory
Rate–distortion optimization
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Quarter-pixel motion
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Compressed video sensing (CVS) is one of the 5G application of compressed sensing (CS) to video coding. Block-based residual reconstruction is used in CVS to explore temporal redundancy in videos. However, most current studies on CVS focus on random measurements without quantization, and thus they are not suitable for practical applications. In this study, an efficient rate-control scheme combining measurement rate and quantization for residual reconstruction in CVS is proposed. The quantization effects on CS measurements and recovery for video signals are first analyzed. Based on this, a mathematical relationship between quantitative distortion (QD), sampling rate (SR), and the quantization parameter (QP) is derived. Moreover, a novel distortion model that exhibits the relationship between QD, SR, and QP is presented, if statistical independency between the QD and the CS reconstruction distortion is assumed. Then, using this model, a rate–distortion (RD) optimized rate allocation algorithm is proposed, whereby it is possible to derive the values of SR and QP that maximize visual quality according to the available channel bandwidth.
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In this article we provide an overview of rate-distortion (R-D) based optimization techniques and their practical application to image and video coding. We begin with a short discussion of classical rate-distortion theory and then we show how in many practical coding scenarios, such as in standards-compliant coding environments, resource allocation can be put in an R-D framework. We then introduce two popular techniques for resource allocation, namely, Lagrangian optimization and dynamic programming. After a discussion of these techniques as well as some of their extensions, we conclude with a quick review of literature in these areas citing a number of applications related to image and video compression and transmission.
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The general problem of optimized video encoding has received a great deal of attention in recent years. This paper focuses on the optimization of video coding with frameskip. We propose models that estimate the distortion for coded frames as well as non-coded frames. Using these models in conjunction with well-know models that estimate the rate allows us to formulate a rate control problem that trades-off spatial and temporal quality. Simulation results indicate moderate improvements for low motion test sequences.
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