An efficient spatial prediction-based image compression scheme

An efficient spatial prediction-based progressive image compression scheme is developed in this paper. The proposed scheme consists of two phases, namely, the prediction phase and the quantization phase. In the prediction phase, information of the nearest neighbor pixels is utilized to predict the center pixel. Next in-place processes are taken, i.e., the resulting prediction error is stored in the same memory location as the predicted pixel. Thus, the temporary storage space required is significantly reduced in the encoding process as well as decoding process. The prediction scheme generates prediction error images with hierarchical structure, which can employ the result of many existing quantization schemes, such as EZW[I] and SPIHT[2] algorithms. As a result, a progressive coding feature is obtained in a straightforward manner. In the quantization phase, we extend the multilevel threshold scheme. Not only the pixel intensity value itself but also level significance is taken into account. In the experimental testing, we illustrate that the proposed scheme yields compression quality advantages. It outperforms several existing image compression schemes. Furthermore, the proposed scheme can be realized by only integer addition and shift operations. Tremendous amounts of computation-saving are achieved. The above features make the proposed image compression scheme beneficial to the areas of real-time applications and wireless transmission in limited bandwidth and low computation power environments.