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Compressed sensing (CS) has been proposed for more efficient signal compression and recovery at theoretical level. This paper proposes a new image/video coding approach combining the CS theory into the traditional discrete cosine transform (DCT) based coding method to achieve better compression efficiency for spatially sparse signal. Furthermore, this new(More)
This paper presents a principled and practical method for the computation of visual saliency of spatiotemporal events in full motion videos. Based on the assumption that uniqueness or informative-ness correlates with saliency, our model predicts the saliency of a spatiotemporal event based on the information it contains. To compute the uniqueness of the(More)
— Existing video coding methods can cause visual quality and buffer occupancy to fluctuate significantly at scene cuts. To address this problem, we have developed a novel visual attention based adaptive bit allocation method. We first perform scene cut detection to extract frames in the vicinities of dramatic scene changes; we then perform visual saliency(More)
We propose an efficient compression algorithm for massive models, which consist of a large number of small to medium sized connected components. It is based on efficiently exploiting repetitive patterns in the input model. Compared with [Shikhare et al. 2001], the state-of-the-art work for utilizing repetitive patterns for compressing massive models, our(More)
The Aspect ratio is the fractional relation of the width of a video image compared to its height. The paper is focused on solving the problem that how to play various kinds of videos on the device screen with different aspect ratio from them. It is the first time to introduce saliency model, which can take human attention into account, into the aspect ratio(More)