Blending Models for Image Enhancement and Coding
Surface representation with polynomial blending functions for enhancement and coding of images and VRML models
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We propose practical algorithms for efficient representation of images. Polynomial blending functions are used for image coding and enhancement. We use this surface model to represent image regions resulting in high perceived quality. We show that the image artifacts originated by quantization can be drastically reduced by using a representation based on blending functions. They are designed to mitigate the quantization noise generated by lossy image coding techniques like JPEG-DCT and JPEG-LS. A new recursive triangular partitioning (RTP) is proposed to represent an image by triangular blendi...
We propose practical algorithms for efficient representation of images. Polynomial blending functions are used for image coding and enhancement. We use this surface model to represent image regions resulting in high perceived quality. We show that the image artifacts originated by quantization can be drastically reduced by using a representation based on blending functions. They are designed to mitigate the quantization noise generated by lossy image coding techniques like JPEG-DCT and JPEG-LS. A new recursive triangular partitioning (RTP) is proposed to represent an image by triangular blending surfaces. Efficient triangular blending models are proposed. The resulting coding algorithm becomes competitive to the state-of-art algorithms based on JPEG-DCT and wavelets. We investigate the problem of optimally quantizing the control points which define the blending surfaces. A greedy algorithm is proposed to quantize 3D coordinates representing objects described in VRML. The resulting coding performance is superior to Lempel-Ziv coding algorithm. This book describe practical algorithms based on blending surfaces applied to image coding and enhancement of lossy compressed images.
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