A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Video Compression Standards: Algorithms and Architectures
Image and Video Compression Standards: Algorithms and Architectures
Pulse discrete cosine transform for saliency-based visual attention
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
IEEE Transactions on Image Processing
Visual attention guided bit allocation in video compression
Image and Vision Computing
Saliency Based on Multi-scale Ratio of Dissimilarity
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Information theory-based shot cut/fade detection and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
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.