The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Classified adaptive prediction and entropy coding for lossless coding of images
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Glicbawls?? Grey Level Image Compression by Adaptive Weighted Least Squares
DCC '01 Proceedings of the Data Compression Conference
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
IEEE Transactions on Image Processing
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In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.