Multi-resolutional human visual perception optimized pathology image progressive coding based on JPEG2000

Abstract

As one of the most studied areas, telemedicine allows doctors and pathologists to share their intelligence all over the world. To achieve the highest efficiency of remote pathology image browsing, especially for the whole-slide pathology image (WSI) big data, we develop a method to conduct the image coding based on the human visual quality for multiple displaying resolutions. In this paper, we establish a multi-resolutional human visual sensitivity model of Wavelet transform coefficients, based on the JPEG2000 standard and the human visual quality characters. With the analysis of the visual masking effect, we further propose a visibility threshold (VT) model for the human visual sensitivity optimized image coding of the WSIs, which considers multi-resolution and different visual qualities jointly. The proposed image coding methods with multiple quality or resolution layers can also be implemented for progressive image transmission and decoding. Compared with the conventional MSE optimized image coding algorithm, the decoded pathology image quality obtained from certain amounts of coded data using the proposed algorithm was proved to be improved from the aspect of visual perception through both objective and subjective evaluations. The perception-optimized progressive pathology image transmission and decoding were also verified with the combination of the proposed VT model and the JPIP protocol. With the proposed method, a remote medical server can adaptively transmit the image data according to the client’s subjective requirements for the remote browsing of pathology image big data, leading to improved efficiency of diagnosis in telemedicine.