Quality issues for archival of ancient documents

This paper reports about a study concerning the application of error bounded encoding to lossy image compression of ancient documents handwritten on parchments. Images are acquired in the RGB color space and previously transformed in the YUV color coordinate system before coding. The coding algorithm, named RLP, considered here is based on a classified DPCM enhanced by a fuzzy clustering initialization and followed by context based statistical modeling and arithmetic coding of prediction residuals that are quantized with user defined odd step sizes to allow rate control with a minimum peak error over the whole image, so as to exactly limit local distortions. Each YUV component is coded separately; after decoding images are transformed back to RGB color space and compared with the originals in order to quantify distortions. A relationship bounding the peak errors in the RGB color space once the peak error is fixed in the YUV color space is derived. An algorithm originally designed for estimating signal-dependent noise parameters and used to obtain useful information about the images of the documents is also reported in the paper. The performances of the coding method are superior with respect to conventional DPCM schemes thanks to its flexibility and robustness to changes in type of images. For the compression ratios requested by this application the gain of RLP over JPEG is consistent: nearly 2 dB and 5 dB in PSNR for compression ratios of 10 and 5 respectively.