Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
IEEE Transactions on Image Processing
In-Camera Automation of Photographic Composition Rules
IEEE Transactions on Image Processing
Super-resolution still and video reconstruction from MPEG-coded video
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Low-power uniform video acquisition with super-resolution reconstruction
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Denoising for Multiple Image Copies through Joint Sparse Representation
Journal of Mathematical Imaging and Vision
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Motivated by the optimization of power and improvement of video resolution, this paper proposes a content-based adaptive sampling system for video acquisition. Blind sampling suffers from the lack of resolution and blurring. However, use of à priori knowledge can provide intelligent sampling function that will reduce the blur artifacts. This paper proposes an information theoretic criteria-based sampling function. Higher sampling is proposed at high motion and edge regions while lower sampling at the low-frequency regions. This helps in providing better resolution with lower power consumption. Previous researches focus on enhancing the coding performance after the video acquisition stage. The proposed adaptive sampling scheme naturally performs super resolution without requiring extensive postprocessing. The proposed scheme has been tested on ten exemplary video sequences. Quality of the proposed adaptive sampling method is over 10-16 dB better than the coarsely sampled video. The power savings is ≅ 30-40% compared to acquiring the full resolution video.