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Visual saliency extraction from compressed streams

Abstract : The theoretical ground for visual saliency was established some 35 years ago by Treisman who advanced the integration theory for the human visual system: in any visual content, some regions are salient (appealing) because of the discrepancy between their features (intensity, color, texture, motion) and the features of their surrounding areas. This present thesis offers a comprehensive methodological and experimental framework for extracting the salient regions directly from video compressed streams (namely MPEG-4 AVC and HEVC), with minimal decoding operations. Note that saliency extraction from compressed domain is a priori a conceptual contradiction. On the one hand, as suggested by Treisman, saliency is given by visual singularities in the video content. On the other hand, in order to eliminate the visual redundancy, the compressed streams are no longer expected to feature singularities. The thesis also brings to light the practical benefit of the compressed domain saliency extraction. In this respect, the case of robust video watermarking is targeted and it is demonstrated that the saliency acts as an optimization tool, allowing the transparency to be increased (for prescribed quantity of inserted information and robustness against attacks) while decreasing the overall computational complexity. As an overall conclusion, the thesis methodologically and experimentally demonstrates that although the MPEG-4 AVC and the HEVC standards do not explicitly rely on any visual saliency principle, their stream syntax elements preserve this remarkable property linking the digital representation of the video to sophisticated psycho-cognitive mechanisms
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Submitted on : Thursday, September 28, 2017 - 10:36:06 AM
Last modification on : Monday, August 24, 2020 - 4:16:03 PM
Long-term archiving on: : Friday, December 29, 2017 - 2:56:43 PM


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  • HAL Id : tel-01597061, version 1


Marwa Ammar. Visual saliency extraction from compressed streams. Image Processing [eess.IV]. Institut National des Télécommunications, 2017. English. ⟨NNT : 2017TELE0012⟩. ⟨tel-01597061⟩



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