Vector quantization of images based on a composite source model
Thesis/Dissertation
·
OSTI ID:5253877
Compression of digitized pictures is finding increasing application in teleconferencing, archiving, and remote sensing. The potential capability of Vector Quantization, a relatively new source coding technique, for the compression of still, monochromatic images is studied here. A vector quantizer operates on blocks (vectors) of contiguous samples of the input signal - in this case, an image. As the block size increases, the performance of vector quantization approaches the best that is theoretically possible, but at the cost of an exponential growth in computational complexity. A coder based on vector quantization, with moderate block sizes, which performs better than existing coders of comparable complexity was developed. Since blocks of samples, rather than individual ones, are treated as atomic entities in vector quantization, a new vector model for images called the Composite Source Model. Each block is viewed as being the output of one of a bank of subsources selected by a switch. Each subsource generates blocks of a distinct perceptual type, e.g., blocks with an edge at a particular orientation. Based on the new model, the author proposed a new coding method called Classified Vector Quantization. In this method, each block of samples in an image is classified to determine which subsource or class it belongs to.
- Research Organization:
- California Univ., Santa Barbara (USA)
- OSTI ID:
- 5253877
- Country of Publication:
- United States
- Language:
- English
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