Optimizing Algorithms for Region-of-Interest Video Compression, with Application to Mobile Telehealth

Wireless communication of video poses constraints on information capacity. Region-of-interest (ROI) video coding provides higher quality in the ROI, but poorer quality in the background (BKGRND), for a given total bitrate (TBR). Researchers, including the authors, have also proposed more graceful quality management methods, using what is referred to here as an extended-region-of-interest (EROI). We consider three levels of losslessness-mathematical, diagnostic, and perceptual, with the goal of associating them with the above-mentioned regions. We describe work in progress aimed at optimizing an elastic expert system based on the above methodology, with telehealth video as its anchor. The optimizations are threefold-user, perceptual, and network oriented, and are incorporated in the rate control algorithm. We propose a rate control method where, unlike conventional methods, bit allocation is shifted from the frame level to individual regions within the frame. Thereafter, the above-mentioned criteria are used to determine regional bit allocation. Peak-signal-to-noise-ratio (PSNR) results show, as expected, that the proposed scheme achieves higher ROI-EROI quality than the verification model VM8 of MPEG4. This is illustrated with four examples of pediatrics video. The value and design of the proposed methodology is being corroborated by subjective testing involving medical experts. We are independently researching another standing issue in the telehealth application, that of low complexity segmentation and tracking of the ROI-EROI boundaries