Predictor selection using quantization intervals in JPEG2000-Based Scalable Interactive Video (JSIV)

The authors have recently introduced the JPEG2000-Based Scalable Interactive Video (JSIV) paradigm. JSIV relies on JPEG2000 format for providing scalability and accessibility, and on motion compensation and conditional replenishment to exploit temporal redundancy. JSIV can provide considerably better interactivity compared to existing video streaming practices, and can adapt immediately to interactive changes in client interests, such as forward or backward playback and zooming into individual frames. This work extends our previous work by providing server and client policies that can exploit the client's knowledge about the quantization intervals of received samples in selecting a favorable predictor in dyadic hierarchical B-frame arrangement that does not employ motion compensation. We also demonstrate the flexibility of the JSIV paradigm by showing an improved client policy working with a non-improved server policy without any negative impact on reconstructed video.