Image coding using parametric texture synthesis

Thakur, Uday Singh; Ray, Bappaditya

doi:10.1109/mmsp.2016.7813339

Cited by 3 publications

(2 citation statements)

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“…Interested reader may refer to [34] and [36]. For HEVC, there exist also initial investigations about the pyramid based synthesis [124] and motion based synthesis for dynamic textures [125].…”

Section: Top-down Approachesmentioning

confidence: 99%

Perceptual Texture Similarity for Machine Intelligence Applications

Naser

Ricordel

Callet

2017

Visual Content Indexing and Retrieval With Psycho-Visual Models

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Textures are homogeneous visual phenomena commonly appearing in the visual scene. They are usually characterized by randomness with some stationarity. They have been well studied in different domains, such as neuroscience, vision science and computer vision, and showed an excellent performance in many applications for machine intelligence. This book chapter focuses on a special analysis task of textures for expressing texture similarity. This is quite a challenging task, because the similarity highly deviates from point-wise comparison. Texture similarity is key tool for many machine intelligence applications, such as recognition, classification, synthesis and etc. The chapter review the theories of texture perception, and provides a survey about the up-to-date approaches for both static and dynamic textures similarity. The chapter focuses also on the special application of texture similarity in image and video compression, providing the state of the art and prospects

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Section: Top-down Approachesmentioning

confidence: 99%

Perceptual Texture Similarity for Machine Intelligence Applications

Naser

Ricordel

Callet

2017

Visual Content Indexing and Retrieval With Psycho-Visual Models

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“…In this approach, the texture is removed at the encoder side and synthesis parameters are sent to the decoder. Similarly, texture synthesis was used for interpolating textures between pictures [5], or replacing the texture with a simplified version [6], [7], [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic texture synthesis using linear phase shift interpolation

Thakur

Naser

Wien

2016

2016 Picture Coding Symposium (PCS)

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Dynamic texture motions like flowing water, motion of leaves etc. have a complex random character. A sequence containing such a content is challenging to encode even when using state of the art High Efficiency Video Coding (HEVC) especially, if the available bandwidth is limited. It is observed that often when predicting dynamic textures, codec switches to intra prediction. At lower rates, dynamic texture content shows visually annoying blurring and blocking artifacts.For dynamic textures, both spatial and temporal details are perceptually of less importance. This property of the Human Visual System (HVS) can be exploited when coding dynamic texture content, as suggested in this paper. At the encoder, preprocessing is done by skipping even numbered B pictures. At the decoder side, skipped pictures are synthesized using linear phase shift interpolation of the complex wavelet coefficients, from the adjacent already decoded pictures. Subjective evaluation of proposed approach is done by using a pair wise comparison test between the proposed results and the conventional HEVC decoded bitstream at similar bitrates. The evaluation results show that viewers prefer the proposed result over conventional HEVC.

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