Abstract
This paper presents a generic video coding framework with the texture abstraction and completion, inspired by a strong grouping bias of local elements in Gestalt psychology. Abstracting imagery by grouping perceptual salience from anisotropic diffusion, it decomposes video images into two layers composing of semantic components and residual detail. The similarity between textures of abstraction layer is motivated to infer the restoration of missing detail, under the spatio-temporal variation regularity. Through a motion and spatial context of moton, hence, a group of pictures (GOP) is divided into key frames and abstracted frames to form the final compressed data. An abstraction refinement is tuned to improve matching of detail restoration based on bilateral filtering. The proposed approach is more generic without incurring any specific side information, and achieves up to 20% bit saving versus standard H.264 at similar visual quality levels.
Li Song
Professor, IEEE Senior Member
Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.