New vector coding schemes for image and video compression

Vector transform coding (VTC) has been shown to be a promising new technique for image and video compression. Vector transformation (VT) reduces inter-vector correlation and preserves intra-vector correlation much better than scalar-based transforms such as the discrete cosine transform (DCT) so that vector quantization (VQ) in the VT domain can be made more efficient. In addition to finding a good vector transform, another important aspect of VTC is the codebook structure and bit-allocation in the VT domain. Vectors with different indices in the VT domain have very different characteristics. Given a total number of bits, the question is now to construct the codebooks and allocate bits to the vectors so that distortion is minimized. A new multi-layered codebook structure and a dynamic bit-allocation scheme have been developed. The main advantage of this method over a fixed bit-allocation scheme is that distortion is controlled by dynamically allocating more bits to vectors causing larger distortions and less bits to vectors causing smaller subband coding into vector case so that scalar-based operations are replaced by vector-based operations. In VSC, an image is first decomposed into a set of vector subbands (VSBs) using a vector filter bank (VFB), and then VQ is performed on all vectors in each VSB. The proposed VFB not only reduces inter-vector and inter-band correlation, but also preserves intra-vector correlation. This property makes the subsequent VQ much more efficient and allows large coding gain over conventional subband coding.