Super high resolution images with more than 2,000*2.000 pixels will play a very important role in a wide variety of applications of future multimedia communications ranging from electronic publishing to broadcasting. To make communication of super high resolution images practicable, we need to develop image coding techniques that can compress super high resolution images by a factor of 1/10 to 1/20. Among existing image coding techniques, the sub-band coding technique is one of the most suitable techniques. With its applications to high-fidelity compression of super high resolution images,one of the major problem is how to encode high frequency sub-band signals. High frequency sub-band signals are well modeled as having approximately memoryless probability distribution, and hence the best way to solve this problem is to improve the quantization of high frequency sub-band signals.
From the standpoint stated above, the work herein First compares three diferent scalor quantization schemes and improved permutation codes, which the authors have previously developed extending the concept of permutation codes, from the aspect of quantization performance for a memoryless probability distribution that well approximates the real statistical properties of high frequency sub-band signals, and thus demonstrates that at low coding rates improved permutation codes outperform the other scalor quatization schemes and that its superiority decreases as its coding rate increases. Moreover, from the results stated above, the work herein, develops rate-adaptive quantization techniques where the number of bits assigned to each subblock is determined according to the signal variance within the subblock and the proper quantization scheme is chosen from among different types of quantizaton schemes according to the allocated number of bits, and applies them to the high-fidelity encoding of high frequency sub-band signals of super high resolution images to demonstrate their usefulness.