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Image compression with upsampling encodes information to succeedinglyincrease image resolution, for example by encoding differences in FUIF and JPEGXL. It is useful for progressive decoding, also often can improve compressionratio - both for lossless compression and e.g. DC coefficients of lossy.However, the currently used solutions rather do not exploit context dependencefor encoding of such upscaling information. This article discusses simpleinexpensive general techniques for this purpose, which allowed to save onaverage $0.645$ bits/difference (between $0.138$ and $1.489$) for the lastupscaling for 48 standard $512\times 512$ grayscale 8 bit images - compared toassumption of fixed Laplace distribution. Using least squares linear regressionof context to predict center of Laplace distribution gave on average $0.393$bits/difference savings. The remaining savings were obtained by additionallypredicting width of this Laplace distribution, also using just the leastsquares linear regression.  For RGB images, optimization of color transform alone gave mean $\approx4.6\%$ size reduction comparing to standard YCrCb if using fixed transform,$\approx 6.3\%$ if optimizing transform individually for each image. Thenfurther mean $\approx 10\%$ reduction was obtained if predicting Laplaceparameters based on context. The presented simple inexpensive generalmethodology can be also used for different types of data like DCT coefficientsin lossy image compression.

Exploiting context dependence for image compression with upsampling