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IEICE TRANSACTIONS on Fundamentals

Two-Layer Near-Lossless HDR Coding Using Zero-Skip Quantization with Backward Compatibility to JPEG

Hiroyuki KOBAYASHI, Osamu WATANABE, Hitoshi KIYA

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Summary :

We propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, which is the international standard to compress HDR images, adopts a two-layer coding method for backward compatibility to the legacy JPEG standard. However, there are two problems with this two-layer coding method. One is that it does not exhibit better near-lossless performance than other methods for HDR image compression with single-layer structure. The other problem is that the determining the appropriate values of the coding parameters may be required for each input image to achieve good compression performance of near-lossless compression with the two-layer coding method of the JPEG XT. To solve these problems, we focus on a histogram-packing technique that takes into account the histogram sparseness of HDR images. We used zero-skip quantization, which is an extension of the histogram-packing technique proposed for lossless coding, for implementing the proposed near-lossless coding method. The experimental results indicate that the proposed method exhibits not only a better near-lossless compression performance than that of the two-layer coding method of the JPEG XT, but also there are no issue regarding the combination of parameter values without losing backward compatibility to the JPEG standard.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.12 pp.1842-1848
Publication Date
2019/12/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E102.A.1842
Type of Manuscript
Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
Category
Image

Authors

Hiroyuki KOBAYASHI
  Tokyo Metropolitan College of Industrial Technology
Osamu WATANABE
  Takushoku University
Hitoshi KIYA
  Tokyo Metropolitan University

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