A pyramid-based error-bounded encoder: An evaluation on X-ray chest images

Abstract

A novel encoder based on an enhanced Laplacian pyramid is proposed for compression of Medical grey-scale images: major details are prioritized through an adaptive decision rule embedded in a uniform threshold quantizer with noise feedback. The major benefit of this content-driven feedback quantizer is that significant features are straightforwardly propagated throughout the pyramid, thus enhancing compactness and visual quality of the reduced-resolution versions progressively associated with the code stream. Nevertheless, the reconstruction error is determined only by the size of the quantization step at the base level of the pyramid, thereby making it possible for the maximum absolute reconstruction error to be easily and strongly upperbounded ( near-lossless compression), as often required in archiving medical images, for diagnostic and legal purposes. Both lossless and lossy coding show favorable comparisons with JPEG in objective terms, i.e., compression ratios and distortion plots. Lossy coding outperforms JPEG also subjectively, due to the absence of visual impairments and diagnostic artifacts even at very low rates. This feature is also evidenced in a preliminary ROC analysis on a set of X-ray chest images. The pyramid encoder produces compressed images whose diagnostic quality seems to be comparable, for medium rates, to that of the uncompressed versions, and superior to that of the JPEG coded versions.