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, which operates adaptively according to the required resolutions and visual qualities, under the JPEG2000 standard for the whole-slide pathology images (WSIs). 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 effect. The objective metric, HDR-VDP 3.0.6 score was implemented to evaluate to proposed encoding method. The averaged resulting scores across 100 LSCC WSIs in various visual qualities are plotted in Fig. 1, where half of the full resolution was used. The results from the mean-squared-error (MSE) based encoder at the same average bit rates are also shown. It can be seen that the predicted visual quality of decoded image from the proposed method is improved, compared with the 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. A representative comparison of the decoded WSI blocks from the encoders with the adjusted and the non-adjusted VTs is shown in Fig. 2, where the adjusted VTs are shown to retain important features in decoding results while further reduce the coding bit rates.

We propose a compression encoding method to perceptually optimize the image quality based on a novel quality metric, which emulates how the human visual system form opinion of a compressed image. Compared to the existing perceptual-optimized compression methods, which usually aim to minimize the detectability of compression artifacts and are sub-optimal in visually lossless regime, the proposed encoder aims to operate in the visually lossy regime. We implement the proposed encoder within the JPEG 2000 standard, and demonstrate its advantage over both detectability-based and conventional MSE encoders.

In this paper, we propose a visual discrimation model to enable perceptually optimized JPEG200 compression for both near-threshold and supra-threshold regimes. The performance of the proposed approach is validated by comparing it to the conventional Mean Squared Error (MSE)-optimized compression.

Image compression systems that exploit the properties of the Human Visual System (HVS) have been studied extensively over the few decades. For the JPEG2000 image compressionstandard, several methods to optimize perceptual quality have been proposed. In 2013, Han et al. proposed a visually lossless compression approach based on the irreversible pipeline defined in the JPEG2000 standard. In this approach, visibility thresholds were measured using psychovisual experiments. These thresholds were then incorporated in a JPEG2000 encoder to ensure that quantization distortions remain below visible levels in the compressed codestreams. In this work, we investigate the use of a similar approach for the reversible pipeline of the JPEG2000 standard. Our motivation is to allow the creation of a scalable codestream that can provide both visually lossless and numerically lossless representations from a single codestream. By comparing the difference in compression performance between the reversible and irreversible pipelines, we also quantify the overhead associated with the reversible pipeline for visually lossless compression.

In this paper, a compressive detection approach for detecting the presence of multiple Frequency Hopping Spread Spectrum (FHSS) signals is introduced. The large bandwidth produced due to hopping between frequency channels makes interception of FHSS signals challenging. Conventional FHSS detection approaches use channelized radiometers which reduce the sampling rate by dividing the entire FHSS hopping frequency range into sub bands. In this paper, we introduce a detection system which compressively observes the entire FHSS hopping range and can detect not only the presence of FHSS signals but also the number of signals present. Theoretical analysis and simulations are provided to compare the performance of the proposed compressive detection system to a channelized radiometer based detection system.