Human visual system weighted progressive image transmission using lapped orthogonal transform/classified vector quantization

Abstract

A progressive image transmission (PIT) scheme based on the classified transform vector quantization (CVQ) technique using the lapped orthogonal transform (LOT) and human visual system (HVS) weighting is proposed. Conventional block transform coding of images using the discrete cosine transform (DCT) produces, in general, undesirable blocking artifacts at low bit rates. Here image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics with HVS weighting to improve the reconstructed image quality by adaptive bit allocation. The subvectors are vector quantized and transmitted progressively. Coding tests using computer simulations show that the LOT/CVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using PIT/DCTVQ. The LOT/CVQ-based PIT reduces the blocking artifact significantly.