This paper proposes a compression algorithm relying on a classified linear-regression prediction followed by context-based modeling and arithmetic coding of the outcome residuals. Images are partitioned into blocks, e.g., 8×8 or 16×16, and a minimum mean square (MMSE) linear predictor is calculated for each block. Fuzzy clustering is utilized to reduce the number of such predictors. Given a preset number of classes, a Fuzzy-C-Means algorithm produces an initial guess of classified predictors to be fed to an iterative procedure which classifies pixel blocks simultaneously refining the associated predictors. All the predictors are transmitted along with the label of each block. Coding time are affordable thanks to fast convergence of the iterative algorithms. Decoding is always performed in real time. The compression scheme provides impressive performances, especially when applied to X-ray images.
Lossless image compression based on a fuzzy-clustered prediction / Aiazzi, Bruno; Baronti, Stefano; Alparone, Luciano. - In: IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS PROCEEDINGS. - ISSN 0271-4302. - STAMPA. - 4:(1999), pp. 9-12. (Intervento presentato al convegno Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS '99 tenutosi a Orlando, FL, USA nel 30 May - 2 June 1999) [10.1109/ISCAS.1999.779930].
Lossless image compression based on a fuzzy-clustered prediction
ALPARONE, LUCIANO
1999
Abstract
This paper proposes a compression algorithm relying on a classified linear-regression prediction followed by context-based modeling and arithmetic coding of the outcome residuals. Images are partitioned into blocks, e.g., 8×8 or 16×16, and a minimum mean square (MMSE) linear predictor is calculated for each block. Fuzzy clustering is utilized to reduce the number of such predictors. Given a preset number of classes, a Fuzzy-C-Means algorithm produces an initial guess of classified predictors to be fed to an iterative procedure which classifies pixel blocks simultaneously refining the associated predictors. All the predictors are transmitted along with the label of each block. Coding time are affordable thanks to fast convergence of the iterative algorithms. Decoding is always performed in real time. The compression scheme provides impressive performances, especially when applied to X-ray images.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.