Adaptive context formation for linear prediction of image data

Abstract

Images are typically non-stationary signals. If prediction is applied in a linear fashion, it must be combined with a technique which takes this characteristic into account. In general, images can be either regarded as piecewise two-dimensional autoregressive processes or they are handled in a block-wise manner.This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to de-interleave the sequence and to compute prediction weights for each sub-source separately. The proposed approach adaptively determines the sub-sources based on the textures within the images.The prediction method is incorporated in a framework for lossless image compression. It is based on least-mean-square filtering and achieves prediction-error entropies, which are comparable to those of least-squares approaches. In combination with a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of different natural images.

Reversible Color Spaces without Increased Bit Depth and Their Adaptive Selection

“…The question is, whether this observation can also be generalized for other compression systems. For this, the investigations have been repeated using the compression system proposed in [13], which uses a more sophisticated context-based linear prediction (CoBaLP2) technique in combination with arithmetic coding. 1 It can clearly be seen in Table III that the performance of this compression system is much higher compared to LOCO-I.…”