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Proceedings Paper

Analysis of prediction algorithms for residual compression in a lossy to lossless scalable video coding system based on HEVC
Author(s): Andreas Heindel; Eugen Wige; André Kaup
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Paper Abstract

Lossless image and video compression is required in many professional applications. However, lossless coding results in a high data rate, which leads to a long wait for the user when the channel capacity is limited. To overcome this problem, scalable lossless coding is an elegant solution. It provides a fast accessible preview by a lossy compressed base layer, which can be refined to a lossless output when the enhancement layer is received. Therefore, this paper presents a lossy to lossless scalable coding system where the enhancement layer is coded by means of intra prediction and entropy coding. Several algorithms are evaluated for the prediction step in this paper. It turned out that Sample-based Weighted Prediction is a reasonable choice for usual consumer video sequences and the Median Edge Detection algorithm is better suited for medical content from computed tomography. For both types of sequences the efficiency may be further improved by the much more complex Edge-Directed Prediction algorithm. In the best case, in total only about 2.7% additional data rate has to be invested for scalable coding compared to single-layer JPEG-LS compression for usual consumer video sequences. For the case of the medical sequences scalable coding is even more efficient than JPEG-LS compression for certain values of QP.

Paper Details

Date Published: 23 September 2014
PDF: 13 pages
Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92170M (23 September 2014); doi: 10.1117/12.2061597
Show Author Affiliations
Andreas Heindel, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Eugen Wige, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
André Kaup, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)

Published in SPIE Proceedings Vol. 9217:
Applications of Digital Image Processing XXXVII
Andrew G. Tescher, Editor(s)

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