To achieve high efficiency of remote pathology image browsing in telemedicine, efficient image compression coding is required. In this work, we establish a visibility threshold (VT) model, which considers multi-resolution and different visual qualities jointly. Based on this model, we propose an image coding method under the JPEG2000 standard for the whole-slide pathology images (WSIs), which operates adaptively according to the required resolutions and visual qualities. Particularly, we calculated the average standard deviation of the wavelet coefficient from lung squamous cell carcinoma (LSCC) WSIs and implemented in the VT calculations, as well as the masking factor calculation in each wavelet subband to count for the masking fact. The objective metric, HDR-VDP2 techniques were implemented to evaluate to proposed encoding method. Experimental results show that the predicted visual quality of decoded image from the proposed method is improved, compared with the conventional MSE based method. Additionally, we also propose a weighting model to adjust the VTs. The encoder with the adjusted VTs concentrates on retaining the visual quality for the regions, where the lesion probabilities predicted with deep neural networks are high. Experimental results show that the adjusted VTs can retain important features in decoding results while further reduce the coding bit rates.