Improved H.264/AVC coding using texture analysis and synthesis

Ndjiki-Nya, Patrick; Makai, B.; Blattermann, G.; Smolic, Aljoscha; Schwarz, Heiko; Wiegand, Thomas

doi:10.1109/icip.2003.1247378

Cited by 36 publications

(17 citation statements)

References 6 publications

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“…Another texture based video coding approach is by Ndjiki Nya et al [3]. In their scheme the video is divided into texture with non important subjective detail and remainder and an improved coding scheme is proposed using a texture analyzer and synthesizer.…”

Section: Literature Reviewmentioning

confidence: 99%

Texture mapping based video compression scheme

Khandelia

Chaudhury

2010

2010 IEEE International Conference on Image Processing

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We present a video coding technique based on texture synthesis via stitching small patches of previous images as done in texture transfer. Each macroblock is identified as a texture or non texture block by means of an edge based criterion. The texture blocks are reconstructed using the luminance values as the control block information to guide the texture transfer algorithm. By modifying the texture transfer algorithm we ensure both spatial as well as temporal consistency. The non texture blocks and the luminance values for texture blocks can be coded by any encoder and thus our method can act as a pre-processor to any video codec e.g. H.264 and add to the compression obtained.

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Section: Literature Reviewmentioning

confidence: 99%

Texture mapping based video compression scheme

Khandelia

Chaudhury

2010

2010 IEEE International Conference on Image Processing

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“…One of the first approaches for synthesis based coding was introduced by NdjikiNya et al in [120] [121]. The proposed algorithm consists of two main functions: texture analyzer (TA) and texture synthesizer (TS).…”

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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“…A general scheme for video coding using texture analysis and synthesis is illustrated in Figure 1. The texture analyzer identifies homogeneous regions in a frame and labels them as textures [5,15,16]. This step can be done by using several texture segmentation strategies that will be introduced later.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality evaluation for texture and motion based video coding

Bosch

Zhu

Delp

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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One approach that can be used to increase compression efficiency beyond the data rates achievable by state-of-the-art video codecs is to use content-based methods whereby not all the pixels are conventionally encoded. An approach to reduce the data rate is to use different coding methods for pixels belonging to areas containing large amount of detail that are costly to encode, for example textures. This can be extended by focusing on the semantic meaning of objects represented in the video sequence and also taking into consideration Human Visual System properties. The goal is to determine where "detail-irrelevant" regions are located in the frame and synthesize them with acceptable perceptual quality. In this paper, we discuss the effects and trade-offs of these techniques based on a set of perceptual experiments and analyze how these areas can influence the viewer's attention.

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Improved H.264/AVC coding using texture analysis and synthesis

Cited by 36 publications

References 6 publications

Texture mapping based video compression scheme

Texture mapping based video compression scheme

Perceptual Texture Similarity for Machine Intelligence Applications

Perceptual quality evaluation for texture and motion based video coding

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