
Proceedings Paper
Quality constraint and rate-distortion optimization for predictive image codersFormat | Member Price | Non-Member Price |
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Paper Abstract
Next generations of image and video coding methods should of course be efficient in terms of compression, but also
propose advanced functionalities. Among these functionalities such as scalability, lossy and lossless coding, data
protection, Rate Distortion Optimization (RDO) and Rate Control (RC) are key issues. RDO aims at optimizing
compression performances, while RC mechanism enables to exactly compress at a given rate. A less common
functionality than RC, but certainly more helpful, is Quality Control (QC): the constraint is here given by the quality. In
this paper, we introduce a joint solution for RDO and QC applied to a still image codec called Locally Adaptive
Resolution (LAR), providing scalability both in resolution and SNR and based on a multi-resolution structure. The
technique does not require any additional encoding pass. It relies on a modeling and estimation of the prediction errors
obtained in an early work. First, quality constraint is applied and propagated through the whole resolution levels called
pyramid. Then, the quantization parameters are deduced considering inter and intra pyramid level relationships. Results
show that performances of the proposed method are very close to an exhaustive search solution.
Paper Details
Date Published: 19 February 2013
PDF: 9 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550D (19 February 2013); doi: 10.1117/12.2001655
Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
PDF: 9 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550D (19 February 2013); doi: 10.1117/12.2001655
Show Author Affiliations
Khouloud Samrouth, Institut d’Électronique et de Télécommunications de Rennes Lab., CNRS (France)
François Pasteau, Institut d’Électronique et de Télécommunications de Rennes Lab., CNRS (France)
François Pasteau, Institut d’Électronique et de Télécommunications de Rennes Lab., CNRS (France)
Olivier Deforges, Institut d’Électronique et de Télécommunications de Rennes Lab., CNRS (France)
Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
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