Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/163183
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Title

Adaptive Lagrange multiplier estimation algorithm in HEVC

AuthorsGonzáez de Suso, J. L.; Martínez-Enríquez, Eduardo CSIC ORCID; Díaz de María, F.
KeywordsVideo coding
Source coding
Rate-distortion optimization
Motion estimation
HEVC
Issue Date27-Apr-2017
PublisherElsevier
CitationSignal Processing: Image Communication 56: 40-51 (2017)
AbstractThe latest High Efficiency Video Coding (HEVC) standard relies on a large number of coding tools from which the encoder should choose for every coding unit. This optimization process is based on the minimization of a Lagrangian cost function that evaluates the distortion produced and the bit-rate needed to encode each coding unit. The value of the Lagrangian parameter λ, which balances the weight of the rate and distortion terms, is related to the quantization parameter through a model that has been implemented in the HEVC reference software. Nevertheless, in this paper we show that this model can be refined, especially for static background sequences, so that the coding performance of HEVC can be improved by adaptively modifying the relation between λ and the quantization parameter. Specifically, the proposed method (i) determines whether the background of a sequence is static or not by means of a simple classifier; and (ii) when static, it evaluates an exponential regression function to estimate a proper value of the λ parameter. In so doing, the proposed method becomes content-aware, being able to dynamically act on the λ parameter. Experiments conducted over a large set of static and dynamic background video sequences prove that the proposed method achieves an average bit-rate saving of −6.72% (−11.07% for static background video sequences) compared with the reference HM16.0 software, notably outperforming the results of a state-of-the-art method.
Description12 pags., 9 figs., 10 tabs.
Publisher version (URL)http://dx.doi.org/10.1016/j.image.2017.04.010
URIhttp://hdl.handle.net/10261/163183
DOI10.1016/j.image.2017.04.010
Identifiersdoi: 10.1016/j.image.2017.04.010
issn: 0923-5965
Appears in Collections:(CFMAC-IO) Artículos

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