An Improved JPEG Compression Algorithm Based on Sloped-Facet Model of Image Segmentation

2007 ◽  
Author(s):  
Chengyou Wang ◽  
Zhengxin Hou ◽  
Kai He ◽  
Aiping Yang
Keyword(s):  
Image Segmentation ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Facet Model

Image Segmentation Based on Facet Model Fitting

2012 ◽  
Vol 459 ◽  
pp. 35-39
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan ◽  
Zhi Hong Yin
Keyword(s):  
Image Segmentation ◽  
Model Fitting ◽  
Facet Model

Image Segmentation Based on Facet Model Fitting

2012 ◽  
Vol 459 ◽  
pp. 35-39
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan ◽  
Zhi Hong Yin
Keyword(s):  
Image Segmentation ◽  
Model Fitting ◽  
Region Growing ◽  
Error Distribution ◽  
Segmentation Algorithm ◽  
Seed Region ◽  
Region Merging ◽  
Fitting Error ◽  
Image Segmentation Algorithm ◽  
Facet Model

The image segmentation algorithm based on facet model fitting is proposed, we firstly employ the facet model to fit the image intensity, and then calculate the fitting error. After acquiring seed segmentation region from the fitting error distribution, the region growing algorithm is implemented to enlarge the seed region to some region boundary. Finally, a new region merging algorithm is implemented to merge adjacent regipons into some large regions. Experiment results intestify the correctness of our proposed segmentation algorithm

scholarly journals Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sebastiano Battiato ◽  
Oliver Giudice ◽  
Francesco Guarnera ◽  
Giovanni Puglisi
Keyword(s):  
Image Quality ◽  
Discrete Cosine Transform ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Experimental Results ◽  
The Other ◽  
Cosine Transform ◽  
Novel Technique ◽  
Compressed Images ◽  
Acquisition Device

AbstractThe JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.

Digital Camera Source Identification Through JPEG Quantisation

2011 ◽  
pp. 291-313 ◽  
Author(s):  
Matthew James Sorrell
Keyword(s):  
Source Identification ◽  
Digital Camera ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Experimental Results ◽  
Photographic Image ◽  
Source Camera Identification ◽  
Camera Identification ◽  
High Level ◽  
Camera Source Identification

We propose that the implementation of the JPEG compression algorithm represents a manufacturer and model-series specific means of identification of the source camera of a digital photographic image. Experimental results based on a database of over 5,000 photographs from 27 camera models by 10 brands shows that the choice of JPEG quantisation table, in particular, acts as an effective discriminator between model series with a high level of differentiation. Furthermore, we demonstrate that even after recompression of an image, residual artefacts of double quantisation continue to provide limited means of source camera identification, provided that certain conditions are met. Other common techniques for source camera identification are also introduced, and their strengths and weaknesses are discussed.

scholarly journals DISCRETE ATOMIC COMPRESSION OF DIGITAL IMAGES

2018 ◽  
pp. 17-33 ◽  
Author(s):  
Iryna Victorivna Brysina ◽  
Victor Olexandrovych Makarichev
Keyword(s):  
Approximation Theory ◽  
Compression Ratio ◽  
Function Theory ◽  
Digital Images ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Aerial Images ◽  
Approximation Properties ◽  
Compression Process ◽  
Compression Scheme

The subject matter of this paper is the discrete atomic compression (DAC) of digital images, which is a lossy compression process based on the discrete atomic transform (DAT). The goal is to investigate the efficiency of the DAC algorithm. We solve the following tasks: to develop a general compression scheme using discrete atomic transform and to compare the results of DAC and JPEG algorithms. In this article, we use the methods of digital image processing, atomic function theory, and approximation theory. To compare the efficiency of DAC with the JPEG compression algorithm we use the sets of the classic test images and the classic aerial images. We analyze compression ratio (CR) and loss of quality, using uniform (U), root mean square (RMS) and peak signal to noise ratio (PSNR) metrics. DAC is an algorithm with flexible parameters. In this paper, we use “Optimal” and “Allowable” modes of this algorithm and compare them with the corresponding modes of JPEG. We obtain the following results: 1) DAC is much better than JPEG by the U-criterion of quality loss; 2) there are no significant differences between DAC and JPEG by RMS and PSNR criterions; 3) the compression ratio of DAC is much higher than the compression ratio of JPEG. In other words, the DAC algorithm saves more memory than the JPEG compression algorithm with not worse quality results. These results are due to the fundamental properties of atomic functions such as good approximation properties, the high order of smoothness and existence of locally supported basis in the spaces of atomic functions. Since generalized Fup-functions have the same convenient properties, it is clear that such compression results can be achieved by application of a generalized discrete atomic transform, which is based on these functions. We also discuss the obtained results in the terms of approximation theory and function theory. Conclusions: 1) it is possible to achieve better results with DAC than with JPEG; 2) application of DAC to image compression is more preferable than JPEG in the case when it is planned to use recognition algorithms; 3) further development and investigation of the DAC algorithm are promising

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