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.

International audienceThis paper addresses the problem of designing a global tone mapping operator for rate-distortion optimized backward compatible compression of HDR images. We consider a two layer coding scheme in which a base SDR layer is coded with HEVC, inverse tone mapped and subtracted from the input HDR signal to yield the enhancement HDR layer. The tone mapping curve design is formulated as the minimization of the distortion on the reconstructed HDR signal under the constraint of a total rate cost on both layers, while preserving a good quality for the SDR signal. We first demonstrate that the optimum tone mapping function only depends on the rate of the base SDR layer and that the minimization problem can be separated in two consecutive minimization steps. Experimental results show that the proposed tone mapping optimization yields the best trade-off between rate-distortion performance and quality preservation of the coded SDR

This paper addresses the problem of designing a global tone mapping operator for rate distortion optimized backward compatible compression of high dynamic range (HDR) images. We address the problem of tone mapping design for two different use cases leading to two different minimization problems. The first problem considered is the minimization of the distortion on the reconstructed HDR signal under a rate constraint on the standard dynamic range (SDR) layer. The second problem remains the same minimization with an additional constraint to preserve a good quality for the SDR signal. Both the distortion and rate are expressed as a function of the spatial gradient in HDR images. Experiments show that the proposed rate and distortion models based on the HDR image gradient accurately predict the real image rate and distortion measures. Experimental results show that for the first minimization, the optimal rate-distortion performances are achieved, and that the second optimization yields the best tradeoff between rate-distortion performance and quality preservation of the SDR signal.

blocks of pixels are sequentially coded using spatial or temporal prediction schemes. For each block, a vector of coding parameters has to be selected. In order to limit the complexity of this decision, independence between blocks is assumed, and coding parameters are locally optimized to maximize the coding efficiency. Few studies have investigated the benefits of inter-block dependencies consideration using Joint Rate-Distortion Optimization (JRDO), especially in Intra coding. To the best of our knowledge, maximum achievable gains of such approaches have never been exhibited. In this paper, we propose two JRDO models performing joint optimization of multiple blocks applied to intra prediction mode decision. The proposed models have been evaluated in both H.264/AVC and HEVC standards. These two models enables a bitrate saving with respect to the classical RDO model up to -3.10% and -2.31% in H.264/AVC and HEVC, respectively.

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%.