Prediction-Based Lossless Image Compression

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.