Performance evaluation of superreconstruction from video | Zendy

Gulcin Caner | Zendy; A. Murat Tekalp | Zendy; Wendi Heinzelman | Zendy

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Performance evaluation of superreconstruction from video

Author(s) -

Gulcin Caner,

A. Murat Tekalp,

Wendi Heinzelman

Publication year - 2003

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.476358

Subject(s) - uncompressed video , computer science , computer vision , artificial intelligence , quantization (signal processing) , pixel , rgb color model , discrete cosine transform , image resolution , jpeg , motion estimation , computer graphics (images) , data compression , video processing , image (mathematics) , video tracking

Several algorithms have been proposed to enhance the resolution of a reference image from multiple still images or video that may be captured/stored in raw or compressed format. This paper provides a thorough study of how the performance of the projections onto convex sets (POCS) method is affected by camera parameters, quantization of the pixel-values, motion estimation errors, and quantization in the DCT-domain (when compressed data is used). Experimental results are provided to evaluate the practical applicability of super-resolution reconstruction in various scenarios. It has been observed that the quality of the resolution enhancement depends on the quantization of pixel intensity values in the RGB (uncompressed) or YUV (compressed) domains by the video capture device, as well as the accuracy of the estimated motion parameters between successive frames

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