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Model-independent rate control for intra-coding based on piecewise linear approximations
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Sanchez Silva, Victor (2020) Model-independent rate control for intra-coding based on piecewise linear approximations. In: 2020 Data Compression Conference, Salt Lake City, Utah, U.S., 24-27 Mar 2020. Published in: 2020 Data Compression Conference (DCC) ISBN 9781728164588. doi:10.1109/DCC47342.2020.00081
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WRAP-model-independent-rate-control-intra-coding-based-piecewise-linear-approximations-Sanchez-Silva-2020.pdf - Accepted Version - Requires a PDF viewer. Download (719Kb) | Preview |
Official URL: https://doi.org/10.1109/DCC47342.2020.00081
Abstract
This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding that departs from using trained models to approximate the rate-distortion (R-D) characteristics of the video sequence. Our algorithm employs piecewise linear approximations of the rate-distortion (R-D) curve of a frame at the block-level. Specifically, it employs information about the rate and distortion of already compressed blocks within the current frame to linearly approximate the slope of the R-D curve of each block. The proposed algorithm is implemented in the High-Efficiency Video Coding (H.265/HEVC) standard and compared with its current RC algorithm, which is based on a trained model. Evaluations on a variety of intra-coded sequences show that the proposed RC algorithm not only attains the overall target bit rate more accurately than the RC algorithm used by H.265/HEVC algorithm but is also capable of encoding each I-frame at a more constant bit rate according to the overall bit budget.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Coding theory , Rate distortion theory, Approximation theory | ||||||
Journal or Publication Title: | 2020 Data Compression Conference (DCC) | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781728164588 | ||||||
Official Date: | 2 June 2020 | ||||||
Dates: |
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DOI: | 10.1109/DCC47342.2020.00081 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 5 March 2020 | ||||||
Date of first compliant Open Access: | 10 March 2020 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 2020 Data Compression Conference | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Salt Lake City, Utah, U.S. | ||||||
Date(s) of Event: | 24-27 Mar 2020 | ||||||
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