Lossless image compression using vector prediction based on spectral correlation

In this paper we present a new way to exploit optimal vector prediction theory for lossless compression of color images. To compress color images, the spectral correlation is usually reduced using a color space transformation (e.g., from RGB colour space to YUV Ricoh colour space, or to YCbCr when small losses are allowed). In this work, we exploit the spectral correlation to develop an optimal vector predictor in order to reduce the entropy of the residual image. To this purpose, we consider a pixel as a vector of the three components R, G, and B, and we predict this vector. As a result, we obtain an improvement of the compression ratio at the cost of an increase in the computational complexity. Some techniques to reduce the computational cost are presented. A comparison is carried out with scalar version of GLICBAWLS and JPLG-LS.