Classified transform coding of images using vector quantization

An adaptive image coding scheme, called classified transform vector quantization, is proposed. It efficiently exploits correlation in large image blocks by taking advantage of transform coding (TC) and vector quantization (VQ), while overcoming the suboptimalities of TC and avoiding the complexity obstacle of VQ. After local mean luminance values are removed in the spatial domain using two-stage interpolative VQ, the residual errors are encoded in the transform domain by means of perceptual block classification and adaptive subvector construction. This scheme avoids the use of scalar quantization of DC coefficients in the transform domain and yet substantially reduces the blocking effect that tends to arise at low bit rates. Good reconstructed images have been obtained at rates between 0.3 and 0.4 bits/pixel, depending on the nature of the test images. The technique also permits progressive image transmission and reproduces errorless images with compression of about 5.0 bits/pixel. Experimental results indicate that an efficient bit allocation in the coding process produces a substantial improvement in performance.>