This paper presents an error bounded encoder suitable for near lossless image compression. The scheme is a classified spatial DPCM, enhanced by a fuzzy clustered initialization and an iterative joint adjustment of predictors and block partition into classes, followed by context based statistical modeling and arithmetic coding of prediction residuals. Prediction errors are quantized with user defined odd step sizes in order to allow rate control with a minimum peak error over the whole image, so as to exactly limit the local distortion. The performances of the method are superior with respect to other similar schemes, thanks to its flexibility and robustness to changes in type of image and desired distortion level. Decoding is always performed in real time, as predictors are trained at the encoder only.
['Bruno Aiazzi', 'Stefano Baronti', 'Luciano Alparone']
Near lossless image compression by relaxation labeled prediction