Gradient edge detection predictor for image lossless compression

There are many examples of digital image processing where lossless image compression is necessarily, due to the costs of data acquisition or legal issues, such as aerial and medical imaging. Need for lossless compression of large amounts of data requires speed and efficiency, so predictive methods are chosen before transform-based methods. Predictive methods rely on prediction, context modeling and entropy coding. Predictor is the first and the most important step which removes a large amount of spatial redundancy. The most representative predictors are median edge detection (MED) predictor used in JPEG-LS standard and gradient adjusted predictor (GAP) used in CALIC. This paper presents a novel threshold controlled, gradient edge detection (GED) predictor which combines simplicity of MED and efficiency of GAP. Amount of removed redundancy is estimated with entropy after prediction. Analysis shows that GED gives comparable entropies with much complicated GAP.