A Vector Quantizer for Image Restoration

A vector quantization algorithm is presented which accomplishes image restoration concurrently with image compression. The algorithm is based on nonlinear interpolative vector quantization (NLIVQ). An efficient codebook design procedure is also presented. A theoretical discussion of the algorithm is included along with results from simulations. 1. Introduction Vector quantization (VQ) is another name for what Shannon called block source coding subject to a fidelity criterion [1] . Coding of this type maps consecutive, usually non-overlapping, segments of input data to their best matching entry in a codebook of reproduction vectors. In the context of image coding, VQ is generally considered a data compression technique. However, VQ algorithms have been presented which perform other signal processing tasks concurrently with compression. These span the range from speech processing tasks such as speaker recognition and noise suppression, to image processing tasks like half-toning, edge det..