Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Edge-Sensing Predictor in Wavelet Lifting Structures for Lossless Image Coding | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2007 Article ID 19313 7 pages doi 2007 19313 Research Article An Edge-Sensing Predictor in Wavelet Lifting Structures for Lossless Image Coding Omer N. Gerek1 and A. Enis Cetin2 1 Department of Electrical and Electronics Engineering Anadolu University 26470 Eskisehir Turkey 2 Department of Electrical Engineering Bilkent University Bilkent 06533 Ankara Turkey Received 25 August 2006 Revised 23 November 2006 Accepted 5 January 2007 Recommended by Beatrice Pesquet-Popescu The introduction of lifting implementations for image wavelet decomposition generated possibilities of several applications and several adaptive decomposition variations. The prediction step of a lifting stage constitutes the interesting part of the decomposition since it aims to reduce the energy of one of the decomposition bands by making predictions using the other decomposition band. In that aspect more successful predictions yield better efficiency in terms of reduced energy in the lower band. In this work we present a prediction filter whose prediction domain pixels are selected adaptively according to the local edge characteristics of the image. By judicuously selecting the prediction domain from pixels that are expected to have closer relation to the estimated pixel the prediction error signal energy is reduced. In order to keep the adaptation rule symmetric for the encoder and the decoder sides lossless compression applications are examined. Experimental results show that the proposed algorithm provides good compression results. Furthermore the edge calculation is computationally inexpensive and comparable to the famous Daubechies 5 3 lifting implementation. Copyright 2007 O. N. Gerek and A. E. Cetin. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly
