Multi-frame optimized quantization for high efficiency video coding

Winken, Martin; Roth, A.; Schwarz, Heiko; Wiegand, Thomas

doi:10.1109/pcs.2015.7170067

Cited by 9 publications

(6 citation statements)

References 85 publications

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“…Primal-Dual Proximal Algorithm is used to solve the convex optimization problem and achieve near optimal Rate-Control (RC). In [19] Wiken et al measure the A c c e p t e d M a n u s c r i p t dependencies between coefficients levels after Discrete Cosine Transform (DCT)/Discrete Sine Transform (DST), leading to an optimization problem solved by an iterative approach. Bichon et al [20], [21] consider Intra prediction mode estimation as a joint optimization of spatially close Prediction Units (PUs).…”

Section: B Global Rdo Solutionsmentioning

confidence: 99%

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Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Bichon¹,

Tanou²,

Ropert³

et al. 2019

IEEE Trans. on Image Process.

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Optimal adaptive quantization is one of the key points to optimize the coding efficiency of video encoders. Latest block-based video compression standards, such as High Efficiency Video Coding (HEVC), extensively use predictive coding techniques that create dependencies between blocks and increase the complexity of optimal block quantizers search. Specifically, the motion compensation is responsible for a dependency network connecting all blocks of the same GOP together. In this paper, this dependency network is estimated by a temporal distortion propagation model and an accurate estimation of Inter and Skip modes probabilities. Optimal quantizers are then designed per block in order to achieve the global optimization in terms of Rate-Distortion efficiency. By implementing the algorithm into the HEVC reference Model (HM), we report −16.51% PSNR-based and −26.26% SSIM-based average bitrate savings compared to no adaptive quantization. The proposed algorithm outperforms several related methods from state-of-the-art. Moreover, along with the demonstration of optimal quantizer solution, we propose an in-depth analysis of the algorithm behavior. This analysis includes, among others, the relative distribution of rates between frames and the control of quantizers dynamic range.

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Section: B Global Rdo Solutionsmentioning

confidence: 99%

“…Further details about this model can be found in [32]. The R-D Shannon bound is injected into (18) to obtain the optimal λ as (19). Developments are detailed in appendix E. λ = 2 ln(2)U it D it (19) To simplify writing, we define λ as…”

Section: A Local Quantization Problemmentioning

confidence: 99%

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Bichon¹,

Tanou²,

Ropert³

et al. 2019

IEEE Trans. on Image Process.

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show abstract

“…Adaptive quantization has been optimized by modeling dependencies either at the pixel level [10], transform coefficient level [11] or CU level [12], [13]. In [10], Valenzise and Ortega estimate a temporal dependency tree, further used to design an adaptive quantization based on the tree depth.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], Valenzise and Ortega estimate a temporal dependency tree, further used to design an adaptive quantization based on the tree depth. Winken et al [11] measure the dependencies between coefficients levels after Discrete Cosine Transform (DCT)/Discrete Sine Transform (DST), leading to an optimization problem solved by an iterative approach. Ropert et al [12] propose the Rate Distortion Spatio-Temporal Quantization (RDSTQ), as a generalization of Macroblock-Tree framework designed for x264 open-source encoder [13].…”

Section: Introductionmentioning

confidence: 99%

Temporal Adaptive Quantization using Accurate Estimations of Inter and Skip Probabilities

Bichon¹,

Tanou²,

Ropert³

et al. 2018

2018 Picture Coding Symposium (PCS)

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Hybrid video coding systems use spatial and temporal predictions in order to remove redundancies within the video source signal. These predictions create coding-schemerelated dependencies, often neglected for sake of simplicity. The R-D Spatio-Temporal Adaptive Quantization (RDSTQ) solution uses such dependencies to achieve better coding efficiency. It models the temporal distortion propagation by estimating the probability of a Coding Unit (CU) to be Inter coded. Based on this probability, each CU is given a weight depending on its relative importance compared to other CUs. However, the initial approach roughly estimates the Inter probability and does not take into account the Skip mode characteristics in the propagation. It induces important Target Bitrate Deviation (TBD) compared to the reference target rate. This paper provides undeniable improvements of the original RDSTQ model in using a more accurate estimation of the Inter probability. Then a new analytical solution for local quantizers is obtained by introducing the Skip probability of a CU into the temporal distortion propagation model. The proposed solution brings −2.05% BD-BR gain in average over the RDSTQ at low rate, which corresponds to −13.54% BD-BR gain in average against no local quantization. Moreover, the TBD is reduced from 38% to 14%.

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“…In [6], a similar approach is extended for multiple-reference motion prediction and compensation, but again it only considers a "one-depth" dependency between blocks. A computationally complex proposal was made by Winken et al [7]. This solution describes the dependencies between all coefficients levels after DCT/DST transform, for a given set of frames.…”

Section: Introductionmentioning

confidence: 99%

R-D spatio-temporal adaptive quantization based on temporal distortion backpropagation in HEVC

Ropert¹,

Tanou²,

Bichon³

et al. 2017

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

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Nowadays, Rate-Distortion Optimization (RDO) is commonly used in hybrid video coding to maximize coding efficiency. Usually, the rate distortion tradeoff is explicitly computed in offline encoder implementations whereas R(D) model are used in live encoders to select the best decisions at a lower computational cost. For sake of simplicity, this (mathematical) modelling is often performed for each coding unit (CU) individually and independently, obliterating the spatial or temporal dependency between CUs. In this paper, we provide a new spatio-temporal algorithm to compute local quantizers, based on a theoretical framework able to describe the temporal distortion propagation from an R-D standpoint. In particular, we model the temporal distortion propagation making possible the retro accumulations of any (spatial) psycho-visually weighted distortion onto reference images. Using the R(D) Shannon bound, its high bitrate approximation, and a Lagrange optimization, analytical solutions are obtained for the local quantizers and the Lagrange multiplier. The proposed algorithm shows-4.4% BD-BR SSIM gains in average over state-of-the art algorithm in HEVC, using the same SSIM-based psycho-visual function.

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Multi-frame optimized quantization for high efficiency video coding

Cited by 9 publications

References 85 publications

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Temporal Adaptive Quantization using Accurate Estimations of Inter and Skip Probabilities

R-D spatio-temporal adaptive quantization based on temporal distortion backpropagation in HEVC

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