Image magnification based on similarity analogy☆

Zuo-ping, Chen; Zheng-lin, YE; Wang, Shuxun; Peng, Guohua

doi:10.1016/j.chaos.2007.10.031

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Moreover, some hybrid methods combining the hierarchical classification and DWT methods were also proposed to improve the performance of fractal image coding [9,10]. In addition to image compression, fractal coding methods have been also gradually implemented in other image processing applications such as image denoising [11][12][13][14][15][16][17], image retrieval [18][19][20], image hashing [21,22], watermarking [23][24][25], image super resolution [26][27][28][29][30], and MRI images [31].…”

Section: Introductionmentioning

confidence: 99%

Adaptively hybrid fractal image coding

Wang,

Jin,

2024

IET Image Processing

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An adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is 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. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). 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 PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image.

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Section: Introductionmentioning

confidence: 99%

Adaptively hybrid fractal image coding

Wang,

Jin,

2024

IET Image Processing

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Modified decoded image quality prediction method for fractal image coding

Wang,

2024

Computers and Electrical Engineering

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Accelerated decoding method in fractal image coding

Wang

2024

Electronics Letters

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To accelerate the fractal decoding process, a minimum domain block set (MDBS)‐based fast fractal decoding method is proposed here. In fractal encoding process, it is found that there exists a MDBS which can provide the best‐matched domain blocks for all range blocks. In the decoding process, MDBS is first identified before the first iteration. Then, only the range blocks inside MDBS are reconstructed in each of the first to penultimate iterations, and the computations of reconstructing the remaining range blocks outside MDBS can be saved to speedup the decoding process. Finally, all range blocks are reconstructed to obtain the decoded image in the last iteration. Experimental results show that about 5%–17% of total computations in decoding process can be saved.

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Image magnification based on similarity analogy☆

Cited by 5 publications

References 18 publications

Adaptively hybrid fractal image coding

Adaptively hybrid fractal image coding

Modified decoded image quality prediction method for fractal image coding

Accelerated decoding method in fractal image coding

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