Image coding based on classified lapped orthogonal transform-vector quantization

Classified transform coding of images using vector quantization (VQ) has proved to be an efficient technique. Transform VQ combines the energy compaction properties of transform coding and the superior performance of VQ. Classification improves the reconstructed image quality considerably because of adaptive bit allocation. A classified transform VQ technique using the lapped orthogonal transform (LOT) is presented. Image blocks are transformed using the LOT and are classified into four classes based on their structural properties. These are further divided adaptively into subvectors depending on the LOT coefficient statistics as this allows efficient distribution of bits. These subvectors are then vector quantized. Simulation results indicate subjectively improved images with LOT/VQ as compared to DCT/VQ