Statistical Analysis of Residue Signal for Backward Compatible HDR Image Coding

Date

2017-07-29

Author

Kamışlı, Fatih
Koz, Alper
Zerman, Emin
Valenzise, Giuseppe
Dufaux, Frederic

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Backward compatibility to the existing 8-bit displays for HDR image and video compression is an essential requirement for the wide acceptance of HDR technology by the users. While these previous methods mainly focus on the rate-distortion optimization of base layer, the encoding of the residue in the enhancement layer comparatively has received less attention. In this paper, we investigate whether HDR image coding residue exhibits also local anisotropic characteristics or not as in the case of motion compensated LDR video coding residue. The verification of existence of such characteristics, as an essential initial stage in such a research, form a base for the development of directional encoding methods for HDR residue as well. For this purpose, statistical auto-covariance analysis on HDR image coding residue is performed in this paper. Specifically, we first interpret the resulting auto-covariance model parameters for HDR images and then the behavior of these parameters is analyzed with respect to bitrate, spatial activity and dynamic range as the main variables in HDR image coding. The experiments indicate that the HDR residue exhibits local anisotropic characteristics, which enables the use of directional methods for the encoding of enhancement layer in HDR coding.

Subject Keywords

High dynamic range, Residue coding, Local characteristics, Backward compatible, Auto-covariance analysis

URI

https://hdl.handle.net/11511/35447

DOI

https://doi.org/10.1109/siu.2017.7960628

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

F. Kamışlı, A. Koz, E. Zerman, G. Valenzise, and F. Dufaux, “Statistical Analysis of Residue Signal for Backward Compatible HDR Image Coding,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35447.