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Adaptive Image Interpolation Using Weight CVQ

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2 Author(s)
Byung-Ho Cha ; Dept. of Electron. Eng., Sogang Univ., Seoul ; Rae-Hong Park

We propose a novel adaptive image interpolation method based on the weight classified vector quantization (CVQ) using optimal coefficients which are constrained by a quadratic signal class. It overcomes the computational complexity bottleneck of other second-order image interpolation methods such as an edge-directed and an optimal recovery interpolation. Our proposed interpolation method consists of three steps: the coefficient generation step constructing both image patches and constrained coefficient sequences from a set of training images, weight CVQ step generating codevectors from classified image patches, and interpolation step using an equal-average nearest neighbor search (ENNS). Simulation results with exemplary test images demonstrate that our proposed method is superior to a bilinear and a bicubic interpolation. Moreover, its visual quality is comparable to that of the edge-directed and the optimal recovery interpolation, with the much less computational load

Published in:

Multimedia, 2006. ISM'06. Eighth IEEE International Symposium on

Date of Conference:

Dec. 2006