Perceptually Optimized MPEG Compression of Synthetic Video Sequences

This paper addresses the problem of improving the quality performance of synthetic video sequences by means of standard frame-based coders. The proposed technique can exploit both the knowledge of the 3D model and the intermediate information computed during the rendering process. Firstly, objects are classified, either semantically or automatically, according to their importance. Then the object classification is translated into a macroblock classification, with particular attention to object boundaries. The classification influences the encoder parameters selection, for instance, the quantization parameter. In order to maximize the performance, we propose a rate-distortion formulation of the problem. Experimental results compared with model-unaware encoding show that the proposed techniques can deliver consistent visual quality improvements for different synthetic scenarios using the same bitrate or even less. Demo sequences are available at http://media.polito.it/perceptual3d.