Edge-Based Prediction for Lossless Compression of Hyperspectral Images

We present two algorithms for error prediction in lossless compression of hyperspectral images. The algorithms are context-based and non-linear, and use a one-band look-ahead, thus requiring a minimal storage buffer. The first algorithm (NPHI) predicts the pixel in the current band based on the information from its context. Prediction contexts are defined based on the neighboring causal pixels in the current band and the corresponding co-located causal pixels in the reference band. EPHI extends NPHI using edge-based analysis. Prediction is performed by classifying the pixels into edge and non-edge pixels. Each pixel is then predicted using information from pixels in the same edge class within the context. Empirical results show that the proposed methods produce competitive results when compared with other state-of-the-art algorithms with comparable complexity. On average, the edge-based technique (EPHI) produced the best overall result, over the images in the test dataset