Acceleration of fractal video compression using FFT

Chaudhari, R. E.; Dhok, Sanjay B.

doi:10.1109/icact.2013.6710524

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…For image encoding and decoding, a threshold value and Huffman coding used, these methods used to compress satellite images. S. B. Dhok, et al [43] published a new method with full search based on motion estimation technique utilizing FFT that uses a circular cross-correlation approach attemptting to optimize the runtime. The correlation-based similarity metric determines the mean subtracted adjusted coefficient for correlation between the range and the domain block.…”

Section: Related Workmentioning

confidence: 99%

Fast Full-Search Algorithm of Fractal Image Compression for Acceleration Image Processing

Abood

Akkar

Al-Safi

2023

SJUOZ

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A new processing algorithm based on fractal image compression is proposed for image processing efficiency. An image will partition into non-overlapping blocks called range blocks and overlapping blocks called domain blocks, with the domain blocks generally bigger than the range blocks, to achieve a rapid encoding time. This research introduced a new fast full-search algorithm approach that starts the search for the best matching domain in the range block from the closest points in the range blocks and expands the search until an acceptable match is found or the search is completed to save even more encoding time. The proposed fast full-search approach, despite its simplicity, is more efficient than the standard search method. The search reduction, peak signal to noise ratio, compression ratio, and encoding time of the suggested approach are all examined. The proposed method can encode a 512x512 grayscale Lena image in 0.36 seconds, with a total search reduction of 87% according to experimental results.

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Section: Related Workmentioning

confidence: 99%

Fast Full-Search Algorithm of Fractal Image Compression for Acceleration Image Processing

Abood

Akkar

Al-Safi

2023

SJUOZ

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“…The code stream formed by this method is encoded, and the decoded video image quality formed after decoding, inverse quantization, and inverse transform at the decoding end is worse than the original image, and the decoded image quality is poor. Because the H.264 encoding effect is relatively better, in order to improve the quality of intraframe coding, this paper draws on the intra prediction method in the video coding standard H.264/AVC and improves it to some extent [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

HD Video Coding based on Fractal and H.264

Chen¹

2018

Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018)

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This paper studies the HD video and remote sensing image compression algorithm based on fractal and H.264.In order to study the technology of image storage and transmission suitable for HD resolution video. The system further optimizes the intra-frame coding mode currently adopted by H.264, and proposes a new fast intra-block block algorithm to reduce the prediction mode selection. For inter prediction, a new motion estimation and motion compensation algorithm is proposed based on fractal. In order to further reduce the number of coded bits, transform, quantize and entropy encode the fractal residuals. Since the motion vector MV and the fractal coefficients cannot be quantized, their differences are directly entropy encoded. The high-definition video coding system based on fractal and H.264 not only reduces the coding complexity, but also improves the coding efficiency.

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“…Normalized covariance, that is, Zero Mean Normalized Cross Correlation (ZNCC) is a method for determining the structural similarity between two blocks from the image [23]. The best matched domain block having high normalized cross correlation [24,25] value may have large average gray level difference. This difference is reduced to zero or very small value by selecting a proper fractal encoding parameters.…”

Section: Introductionmentioning

confidence: 99%

Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure

Chaudhari

Dhok

2016

Mathematical Problems in Engineering

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Fast normalized covariance based similarity measure for fractal video compression with quadtree partitioning is proposed in this paper. To increase the speed of fractal encoding, a simplified expression of covariance between range and overlapped domain blocks within a search window is implemented in frequency domain. All the covariance coefficients are normalized by using standard deviation of overlapped domain blocks and these are efficiently calculated in one computation by using two different approaches, namely, FFT based and sum table based. Results of these two approaches are compared and they are almost equal to each other in all aspects, except the memory requirement. Based on proposed simplified similarity measure, gray level transformation parameters are computationally modified and isometry transformations are performed using rotation/reflection properties of IFFT. Quadtree decompositions are used for the partitions of larger size of range block, that is, 16 × 16, which is based on target level of motion compensated prediction error. Experimental result shows that proposed method can increase the encoding speed and compression ratio by 66.49% and 9.58%, respectively, as compared to NHEXS method with increase in PSNR by 0.41 dB. Compared to H.264, proposed method can save 20% of compression time with marginal variation in PSNR and compression ratio.

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Acceleration of fractal video compression using FFT

Cited by 8 publications

References 6 publications

Fast Full-Search Algorithm of Fractal Image Compression for Acceleration Image Processing

Fast Full-Search Algorithm of Fractal Image Compression for Acceleration Image Processing

HD Video Coding based on Fractal and H.264

Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure

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