Reversible interframe compression of high-quality color images of paintings

Reversible compression of color images is gaining the ever-increasing attention of multimedia publishing industries for collections of works-of-art. In fact, the availability of high-resolution high-quality multispectral scanners demands robust and efficient coding techniques capable to capture inter-band redundancy without destroying the underlying intra-band correlation. Although DPCM schemes (e.g., lossless JPEG) are employed for reversible compression, their straightforward extension to true-color (e.g., RGB, XYZ) image data usually leads to a negligible coding gain or even to a performance penalty with respect to individual coding of each color component. Previous closest neighbor (PCN) prediction has been recently proposed for lossless data compression of multispectral images, in order to take advantage of inter-band data correlation. The basic idea to predict the value of the current pixel in the current band on the basis of the best zero-order predictor on the previously coded band has been applied by extending the set of predictors to those adopted by lossless JPEG. On a variety of color images, one of which acquired directly from a painting by the VASARI Scanner at the Uffizi Gallery with a very high resolution (20 pel/mm, 8 MSB for each of the XYZ color components), experimental results show that the method is suitable for inter-frame decorrelation and outperforms lossless JPEG and, to a lesser extent, PCN.