NDLI: Two-Stage Multiview Image Compression Using Interview SIFT Matching

Content Provider	IEEE Xplore Digital Library
Author	Huihui Bai Mengmeng Zhang Meiqin Liu Anhong Wang Yao Zhao
Copyright Year	2014
Description	Author affiliation: Beijing Jiaotong Univ., Beijing, China (Huihui Bai; Meiqin Liu; Yao Zhao) \|\| North China Univ. of Technol., Beijing, China (Mengmeng Zhang) \|\| Taiyuan Univ. of Sci. & Technol., Taiyuan, China (Anhong Wang)
Abstract	In this paper, a novel scheme of two-stage multiview image compression is proposed to create two-level reconstructed quality. Differently from the conventional multiview image compression algorithms, SIFT (Scale-Invariant Feature Transform) features matching from interview images are exploited to remove the correlations between multiple views. In the first stage coding, SIFT and RANSAC (RANdom SAmple Consensus) algorithms are combined to calculate the correlation matrix of interview, which then can be developed to obtain the coarse reconstruction of the current view. In the second stage coding, the reconstructed quality can be improved further by using the residual information. The experimental results have shown that at higher compression ratio, the proposed scheme can obtain better rate-distortion performance than intra coding in MVC (Multiview Video Coding). Furthermore, with the change of the compression ratio, the proposed scheme can achieve more stable reconstructed quality.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	398
Ending Page	398
File Size	96095
Page Count	1
File Format	PDF
ISBN	9781479938827
ISSN	10680314
DOI	10.1109/DCC.2014.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Correlation Feature extraction Educational institutions Interviews Roads Cameras image compression Multiview SIFT
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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