A scheme for attentional video compression
Rupesh Gupta,Santanu Chaudhury +1 more
- pp 458-465
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TLDR
An improved, macroblock (MB) level, visual saliency algorithm, aimed at video compression, is presented and a video compression architecture for propagation of saliency values, saving tremendous amount of computation, is proposed.Abstract:
In this paper an improved, macroblock (MB) level, visual saliency algorithm, aimed at video compression, is presented. A Relevance Vector Machine (RVM) is trained over 3 dimensional feature vectors, pertaining to global, local and rarity measures of conspicuity, to yield probabalistic values which form the saliency map. These saliency values are used for non-uniform bit-allocation over video frames. A video compression architecture for propagation of saliency values, saving tremendous amount of computation, is also proposed.read more
Citations
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Journal ArticleDOI
A Newly Developed Ground Truth Dataset for Visual Saliency in Videos
Muhammad Zeeshan,Muhammad Majid,Imran Fareed Nizami,Syed Muhammad Anwar,Ikram Ud Din,Muhammad Khurram Khan +5 more
TL;DR: It is evident from results that multiple kernel learning and spectral residual-based saliency algorithms perform best for different genres and motion-type videos in terms of F-measure and execution time, respectively.
Book ChapterDOI
Applications of Saliency Models
Matei Mancas,Olivier Le Meur +1 more
TL;DR: This chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models, and uses saliency maps to detect the regions which are the less interesting in an image.
Book ChapterDOI
Human Attention Modelization and Data Reduction
TL;DR: The main purpose of the attentional process is to make best use of the parallel processing resources of the authors' brains to identify as quickly as possible those parts of their environment that are key to their survival.
References
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Proceedings ArticleDOI
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Proceedings Article
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