Hybrid edge-based fractal image encoding using K-NN search

Gupta, Ritu; Mehrotra, Deepti; Tyagi, R. K.

doi:10.1007/s11042-022-12631-7

Cited by 3 publications

(4 citation statements)

References 41 publications

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“…For RBSVs, we used 2 and 8 bits to quantize the scaling and offset coefficients, respectively. Four fractal coding methods, Jacquin's [2], Chaurasia's [32], Zheng's [33], and Gupta's methods [34], were adopted to assess the performance of the proposed method by decoded image quality, encoding speed, bpp, and decoding speed.…”

Section: Methodsmentioning

confidence: 99%

“…Then, Hurtgen et al [36] made more detailed classification for the blocks, and the encoding process can be accelerated further. For feature-vector based methods, Chaurasia [32] proposed composite statistical features, Gupta [34] extracted the features of the blocks in the DCT domain. The candidates of best-matched domain blocks can be determined in the feature space, and the block-matching operations were only carried out within the candidates.…”

Section: Related Workmentioning

confidence: 99%

“…Although fractal image coding has many advantages, due to its high computational complexity in encoding process, fast fractal image coding becomes one of the most important topics in this field, and many researchers attempt to overcome this problem by converting exhaustive matching into local matching [32][33][34]. For instance, some researchers proposed the classification based methods.…”

Section: Introductionmentioning

confidence: 99%

“…For RBSVs, the no‐search fractal encoding method was adopted to accelerate the encoding process and reduce bpp. Finally, four fractal encoding methods, Jacquin's [2], Chaurasia's [32], Zheng's [33], and Gupta's methods [34], were adopted to assess the effectiveness of the proposed method. Experimental results show that compared with the previous methods, the proposed method can achieve better decoded image quality, faster encoding speed, fewer bpp and comparable or faster decoding speed.…”

Section: Introductionmentioning

confidence: 99%

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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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