TY - JOUR
T1 - Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images
AU - Hernandez-Cabronero, Miguel
AU - Sanchez, Victor
AU - Blanes, Ian
AU - Auli-Llinas, Francesc
AU - Marcellin, Michael W.
AU - Serra-Sagrista, Joan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - © 1982-2012 IEEE. The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.
AB - © 1982-2012 IEEE. The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.
KW - Color-transform optimization
KW - image compression
KW - whole-slide images
UR - https://ddd.uab.cat/record/200911
U2 - https://doi.org/10.1109/TMI.2018.2852685
DO - https://doi.org/10.1109/TMI.2018.2852685
M3 - Article
C2 - 29994394
VL - 38
SP - 21
EP - 32
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
M1 - 8402229
ER -