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JND-Guided Perceptual Pre-filtering for HEVC Compression of UHDTV Video Contents

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

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Abstract

In this paper, two new perceptual filters are presented as pre-processing techniques to reduce the bitrate of HEVC compressed Ultra high-definition (UHD) video contents at constant visual quality. The proposed perceptual filters rely on two novel adaptive filters (called BilAWA and TBil) which combine the good properties of the bilateral and Adaptive Weighted Averaging (AWA) filters. Moreover, these adaptive filters are guided by a just-noticeable distortion (JND) model to adaptively control the strength of the filtering process, taking into account the properties of the human visual system. Extensive psychovisual evaluation tests conducted on several UHD-TV sequences are presented in detail. Results show that applying the proposed pre-filters prior to HEVC encoding of UHD video contents lead to bitrate savings up to 23% for the same perceived visual quality.

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Acknowledgements

The authors would like to thank Elie de Rudder whom internship was the starting point for this work. They also thank Nicolas Braud from TF1, for the UltraHD video test material, Prof. Sylvie Merviel-Leleu, head of Arenberg Creative Mine, for giving access to the audiovisual equipment necessary for the subjective visual assessment tests. This work has been partially supported by the French ANRT (Cifre # 1098/2010) and Digigram.

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Correspondence to François-Xavier Coudoux .

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Vidal, E., Coudoux, FX., Corlay, P., Guillemot, C. (2017). JND-Guided Perceptual Pre-filtering for HEVC Compression of UHDTV Video Contents. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-70353-4_32

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