Adaptively hybrid fractal image coding

Subjects: Computer Science >> Computer Application Technology submitted time 2023-09-06

Wang, Qiang Jin, Guohua Bi, Sheng

Abstract： In this study, an adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, we found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP), and the remaining RBSVs were encoded with the no-search fractal encoding method. Moreover, an effective method to adaptively divide the range blocks into the above two categories was proposed. Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that, compared with the previous methods, the proposed method can achieve better-decoded image quality with fewer bits per pixel and fewer computations.

Peer Review Status:Awaiting Review

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