Sparse least-squares prediction for intra image coding

Lucas, Luis F. R.; M. M. Rodrigues, Nuno; Pagliari, Carla L.; Silva, Eduardo A. B. da; Faria, Sergio M. M. de

http://hdl.handle.net/10400.8/13248

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Authors

Lucas, Luis F. R.

M. M. Rodrigues, Nuno

Pagliari, Carla L.

Silva, Eduardo A. B. da

Faria, Sergio M. M. de

Abstract(s)

This paper presents a new intra prediction method for efficient image coding, based on linear prediction and sparse representation concepts, denominated sparse least-squares prediction (SLSP). The proposed method uses a low order linear approximation model which may be built inside a predefined large causal region. The high flexibility of the SLSP filter context allows the inclusion of more significant image features into the model for better prediction results. Experiments using an implementation of the proposed method in the state-of-the-art H.265/HEVC algorithm have shown that SLSP is able to improve the coding performance, specially in the presence of complex textures, achieving higher coding gains than other existing intra linear prediction methods. © 2015 IEEE.

Description

Published in: 2015 IEEE International Conference on Image Processing (ICIP). Date of Conference: 27-30 September 2015. Date Added to IEEE Xplore: 10 December 2015. Source title - Proceedings - International Conference on Image Processing, ICIP Vol. - 2015-December, pp. 1115 - 1119; Article number - 7350973.

Keywords

Image Coding Intra Prediction Least-Squares Minimization Sparse Coding

URI

http://hdl.handle.net/10400.8/13248

Citation

L. F. R. Lucas, N. M. M. Rodrigues, C. L. Pagliari, E. A. B. da Silva and S. M. M. de Faria, "Sparse least-squares prediction for intra image coding," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 1115-1119, doi: 10.1109/ICIP.2015.7350973.