Fast lossless color image compressing method using neural network

The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time multimedia system. A novel lossless compression algorithm is researched. A low complexity predictive model is proposed using the correlation of pixels and color components. In the meantime, perception in neural network is used to rectify the prediction values adaptively. It makes the prediction residuals smaller and in a small dynamic scope. Also, a color space transform is used and good decorrelation is obtained in our algorithm. Compared to the new standard JPFG-LS, this predictive model reduces its computational complexity. The compared experimental results have shown that our algorithm has noticeable better performance than the traditional algorithms. Moreover, its speed is faster than the JPEG-LS with negligible performance sacrifice.