Region-based prediction for image compression in the cloud

Jean Bégaint (1, 2) , Dominique Thoreau (1) , Philippe Guillotel (1) , Christine Guillemot (2)

1 Technicolor R & I [Cesson Sévigné]
2 Sirocco - Analysis representation, compression and communication of visual data

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared to current image inter-coding solutions such as HEVC.

Domaines: Traitement du signal et de l'image [eess.SP]

Jean Bégaint, Dominique Thoreau, Philippe Guillotel, Christine Guillemot. Region-based prediction for image compression in the cloud. IEEE Transactions on Image Processing, 2018, 27 (4), pp.1835-1846. ⟨10.1109/TIP.2017.2788192⟩. ⟨hal-01662639⟩