This paper presents a method for iterative minimization of combined residual and prediction error for near-lossless
compression of medical computed tomography acquisitions using pixel-wise least-squares prediction. While most
other lossy state-of-the-art image compression systems like JPEG 2000 make use of transform-based coding, in
lossless coding higher compression ratios can be achieved with plain predictive algorithms like JPEG-LS because
of their non-linear data adaptive energy reduction. Yet, applying these algorithms in lossy coding, simple
quantization usually leads to error propagation and therefore serious quality loss or rate increase, as prediction
accuracy of a pixel value and thus data rate depends on the previously reconstructed image region. The proposed
minimization approach modifies the original image to be coded in a way such that the edge-directed prediction
method from literature may achieve better predictions while introducing only a minimum amount of distortion.
Compared to transform-based coding methods, the distortion introduced by the proposed scheme mostly consists
in noise reduction instead of blurring or the introduction of artificial structures. The method also prevents error
propagation due to the consideration of all pixel dependencies of the prediction. It is shown that, combined
with a context-adaptive arithmetic coder, in high-fidelity coding (i. e., PSNR higher than 55 dB) the proposed
method can achieve higher compression ratios than the transform-based approaches JPEG 2000, H.264/AVC,
and HEVC intra coding.