You can also think of delta encoding as linear predictive coding where only the prediction residual is stored and the predictor of the current sample is the previous sample. This is a fixed linear predictor (not with arbitrary coefficients optimized to data) that can exactly predict constant signals. Run the same linear predictive coding again on the residuals, and you have exactly predicted linear signals. Next round, quadratic signals. Or run a higher-order fixed predictor once to do the same.

Such fixed predictors are listed in the [Shorten paper][1], yours in Eq. 4, and are also included in the FLAC lossless audio codec although not often used. Calculating the best prediction coefficients for each data block and storing them in the block header results in better compression than the use of fixed predictors.

The linear predictor is supposed to do the whitening, making the residuals independent. In lossless compression, what is left to do is to entropy code the residuals, instead of using run-length or other symbol-based encoding that doesn't work so well on noisy signals. Typically, entropy coding assigns longer code words to large residuals, approximately minimizing the mean encoding length for an assumed distribution of the residual values. A variant of Rice coding compatible with signed numbers is typically used.

  [1]: http://mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/robinson_tr156.pdf