Optimal bit allocation in the presence of quantizer feedback

Uz, K.M.; Shapiro, J.M.; Czigler, M.

doi:10.1109/icassp.1993.319828

Cited by 24 publications

(33 citation statements)

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“…The methods proposed in the literature to deal with the frame-level rate allocation are either theoretical in nature [25], computationally demanding [4], approximated [27], or based on the MINMAX criterion [33]. In this paper, we propose an efficient solution to exactly solve the frame-level rate allocation problem formulated with the MINAVE criterion.…”

Section: B Contributionsmentioning

confidence: 99%

“…Within this context, a more theoretical approach dates back to Uz et al [25], who proposed a recursive R-D model to describe the dependency between frames, and approached the frame-level rate allocation from a Lagrangian perspective. This recursive model was later extended at pixel level for heuristically selecting the optimal quantization parameters [26].…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Fiengo

Chierchia

Cagnazzo³

et al. 2017

IEEE Trans. on Image Process.

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Abstract-Optimal rate allocation is among the most challenging tasks to perform in the context of predictive video coding, because of the dependencies between frames induced by motion compensation. In this paper, using a recursive rate-distortion model that explicitly takes into account these dependencies, we approach the frame-level rate allocation as a convex optimization problem. This technique is integrated into the recent HEVC encoder, and tested on several standard sequences. Experiments indicate that the proposed rate allocation ensures a better performance (in the rate-distortion sense) than the standard HEVC rate control, and with a little loss w.r.t. an optimal exhaustive research which is largely compensated by a much shorter execution time.

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Section: B Contributionsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Fiengo

Chierchia

Cagnazzo³

et al. 2017

IEEE Trans. on Image Process.

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“…Even though the actual bit allocation, which is derived from these model assumptions, is rather academical, the authors of [USC93] conclude that, by considering inter-frame dependencies in the rate distortion optimization, "in the case of quantizer feedback, frames that are either easily predicted, or good predictors are encoded to higher quality since propagating quantization errors contribute to the total error in predicted frames. "…”

Section: Model-based Dependent Bit Allocationmentioning

confidence: 99%

“…In [USC93], the theoretical optimal bit allocation in the presence of quantizer feedback under the model assumption of an exponential distortion-rate function is derived, i.e. for the first frame, which does not depend on other frames, the relation between bit rate R 1 and quantization error variance E 1 is modeled as…”

Section: Model-based Dependent Bit Allocationmentioning

confidence: 99%

Multi-frame optimized quantization for high efficiency video coding

Winken

Roth

Schwarz

et al. 2015

2015 Picture Coding Symposium (PCS)

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In video coding, there are inter-frame dependencies due to motion-compensated prediction. The achievable rate distortion performance of an inter-coded frame depends on the coding decisions made during the encoding of its reference frames. Typically, in the encoding of a reference frame, these dependencies are either not considered at all or only via some rough heuristic.In this thesis, a multi-frame transform coefficient optimization method for H.265/ HEVC is developed and studied. The inter-frame dependencies are described using a linear signal model. Based on this model, the optimization problem is cast in the form of an 1 -regularized least squares problem. For solving this problem, an optimization algorithm is developed, which is applicable to H.265/HEVC without imposing excessive demands in terms of computational complexity and memory requirements. A simple functional relationship between the regularization parameter and the quantization paramter is empirically found. The accuracy of the linear signal model is studied, the bit rate savings due to the proposed method are evaluated, and its complexity is assessed. Finally, an extension of the method for spatially scalable video coding using SVC, the scalable extension of H.264/AVC, is presented.iii ZusammenfassungBei der Codierung von Videosignalen ergeben sich aufgrund der bewegungskompensierten Prädiktion Abhängigkeiten zwischen den einzelnen Frames. Die erzielbare Rate-Distortion-Performance eines inter-codierten Frames hängt dadurch von den Codierentscheidungen ab, die bei der Codierung seiner Referenzbilder getroffen wurden. Typischerweise werden diese Abhängigkeiten bei der Codierung eines Referenzbildes entweder gar nicht beachtet oder aber nur mittels einer groben Heuristik.

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“…The goal of the models is to achieve better bit allocation throughout the sequence. Examples of bit rate control schemes that use models can be be found in [20,21,22,23,24,25,26]. For low bit rate applications, a common approach is to fix the frame rate and spatial resolution to be processed by the encoder at levels chosen a priori and then drop frames based on the buffer level to prevent buffer overflow [2,27,28].…”

Section: Conventional Bit Rate Controlmentioning

confidence: 99%

<title>Multidimensional bit-rate control for video communication</title>

Reed

Lim

2000

SPIE Proceedings

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In digital video communications, buffering is required to absorb variations between the source rate and the channel rate. Hence, a bit rate control strategy is necessary to maintain the buffer level. In conventional bit rate control, the buffer level is maintained by adapting the quantization stepsize while the frame rate and spatial resolution remain fixed at levels chosen a priori. This thesis investigates a Multi-Dimensional (M-D) bit rate control where the buffer level is maintained by jointly adapting the frame rate, spatial resolution and quantization stepsize. In contrast to the conventional approach, the frame rate and spatial resolution are chosen automatically during the coding process and can adapt to a nonstationary source.We introduce a fundamental framework to formalize the description of the M-D bufferconstrained allocation problem. Given a set of operating points on a M-D grid to code a nonstationary source in a buffer-constrained environment, we formulate the optimal solution. The formulation allows a skipped frame to be reconstructed from one coded frame using any temporal interpolation method and is shown to be a generalization of formulations considered in the literature. In the case of intraframe coding, a dynamic programming algorithm is introduced to find the optimal solution. The algorithm allows one to compare operational rate-distortion (R-D) bounds of the M-D and conventional approaches. We also discuss how a solution can be obtained for the case of interframe coding using the optimal dynamic programming algorithm for intraframe coding by making an independent allocation approximation.We experiment with zero-order hold and global motion-compensated temporal interpolation and illustrate that the M-D approach can provide bit rate reductions over 50%. We also show that the M-D approach with limited lookahead provides a slightly suboptimal solution that consistently outperforms the conventional approach with full lookahead. While our algorithm is computationally expensive, it can be directly used for nonreal-time encoding, for benchmarking, and as an aid in the development of suboptimal algorithms.

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Optimal bit allocation in the presence of quantizer feedback

Cited by 24 publications

References 0 publications

Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Multi-frame optimized quantization for high efficiency video coding

<title>Multidimensional bit-rate control for video communication</title>

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