Image transform coding using trellis coded quatization through noisy channels

We propose a joint source/channel coding scheme to transmit image through binary noisy channels based on 2-D discrete cosine transform (DCT) and trellis coded quantization (TCQ). When an image is transmitted through noisy channel with high throughput, both image compression and error-resilient coding scheme need to be considered. After the discrete cosine transform, the source image is decomposed into several subsources according to the transform coefficient positions, i.e., the same frequency coefficients in different DCT blocks are grouped together as a single subsource. The mean and variance values are used to construct the scalar codebooks for TCQ. Uniform threshold trellis coded quantizer is constructed to release the complexity and the transform coefficients are quantized by these fixed-rate quantizer and transmitted through noisy channels. No explicit error protection is used. The steepest descent method and iterative schemes are employed to determine the optimum bit allocation among the subsources subject to the constraints of the average coding rate and allowable maximum bits to each sample. Neighborhood relation is employed to limit the searching space when a bit is to be allocated to certain subsource. Simulation results show that the performance is very promising.