RDTC optimized compression of image-based scene representations (Part II): practical coding

Interactive streaming of compressed image-based scene representations requires random access to the reference image data. The degree of interframe dependencies exploited during encoding has an impact on the transmission and decoding time and, at the same time, delimits the (storage) rate-distortion (RD) tradeoff that can be achieved. The transmission data rate and the decoding complexity at the client have received attention in the literature, but their incorporation into the optimization procedure for compression and streaming is missing. If scenario-specific measures are considered, the traditional RD optimization can be extended to a tradeoff between the (storage) rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). In the first part of this sequel of papers, we have theoretically analyzed the RDTC space for the compression of densely sampled image-based scene representations. In this second part, we consider practical RDTC optimization. We propose a modeling and encoding parameter selection procedure that allows us to adapt the compression to scenario-specific properties. The impact of client side caching is considered and evaluated using an experimental testbed. Our results show a significant reduction of the user perceived delay, memory consumption or required minimum channel and storage bitrate for RDTC optimized streams compared to classical RD optimized or independently encoded scene representations.