In-loop denoising of reference frames for lossless coding of noisy image sequences

Wige E, Amon P, Hutter A, Kaup A (2010)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2010

Pages Range: 461-464

Article Number: 5654136

Event location: Hong Kong CN

ISBN: 9781424479948

DOI: 10.1109/ICIP.2010.5654136

Abstract

The major gain in video coding applications compared to single image coding is the use of temporal prediction, which exploits the correlation between adjacent frames. However, in high quality video coding, especially lossless video coding, the compression gain of P-frames over I-Frames becomes very small. The reason for that is that the reference frame for inter prediction is not good enough, and therefore the amount of inter-predicted blocks in a P-Frame becomes relatively small compared to the number of intra-predicted blocks. In order to generate a better predictor for inter-prediction, we propose to remove additive noise from the reference frame using an adaptive Wiener filter. This way, we could achieve a maximum compression gain of 4.6% and an average compression gain of 3.3% in contrast to the H.264/AVC standard for lossless coding of high quality image sequences without affecting the encoding time noticeably. © 2010 IEEE.

Authors with CRIS profile

Eugen Wige Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS) Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS)

Involved external institutions

Siemens AG DE Germany (DE)

How to cite

APA:

Wige, E., Amon, P., Hutter, A., & Kaup, A. (2010). In-loop denoising of reference frames for lossless coding of noisy image sequences. In Proceedings of the 2010 17th IEEE International Conference on Image Processing, ICIP 2010 (pp. 461-464). Hong Kong, CN.

MLA:

Wige, Eugen, et al. "In-loop denoising of reference frames for lossless coding of noisy image sequences." Proceedings of the 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong 2010. 461-464.

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