Proceedings ArticleDOI
Dynamic texture synthesis for video compression
Shruti Bansal,Santanu Chaudhury,Brejesh Lall +2 more
- pp 1-5
TLDR
A dynamic texture based compression scheme is devised for videos for the analysis of motion patterns in a video on the basis of optic flow data and then clusters of different motion patterns are created.Abstract:
In this paper, a dynamic texture based compression scheme is devised for videos. Correspondence analysis is explored for the analysis of motion patterns in a video on the basis of optic flow data and then clusters of different motion patterns are created. Dynamic textures tend to disobey Horn and Schunck's assumption of brightness constancy and hence, optic flow residual is used as an indicator of their presence. The correspondence analysis results and optic flow residual are combined together in a new segmentation scheme. The optic flow data tracks the motion of groups of pixels to generate the flow lines. These flow lines are used in a synthesis scheme for creating an illusion of continuously flowing texture. The integration of this synthesis scheme in the compression format gives us considerable bit stream reduction corresponding to the dynamic texture regions. The scheme is integrated with standard H.264/AVC model, texture regions that do not fall under the above precinct of dynamic textures and non-texture regions are encoded-decoded by the H.264 model directly.read more
Citations
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Journal ArticleDOI
Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies
TL;DR: This article presents an end-to-end neural video coding framework that takes advantage of the stacked DNNs to efficiently and compactly code input raw videos via fully data-driven learning.
Posted Content
Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies.
TL;DR: Wang et al. as discussed by the authors presented an end-to-end neural video coding framework that takes advantage of the stacked DNNs to efficiently and compactly code input raw videos via fully data-driven learning.
References
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Journal ArticleDOI
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TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Proceedings ArticleDOI
Determining Optical Flow
TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Proceedings ArticleDOI
Video textures
TL;DR: This paper presents techniques for analyzing a video clip to extract its structure, and for synthesizing a new, similar looking video of arbitrary length, and combines video textures with view morphing techniques to obtain 3D video textures.
Journal ArticleDOI
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