Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images

1993 ◽  
Vol 3 (6) ◽  
pp. 421-432 ◽  
Author(s):  
Yongyi Yang ◽  
N.P. Galatsanos ◽  
A.K. Katsaggelos
Keyword(s):  
Discrete Cosine Transform ◽  
Blocking Artifacts ◽  
Cosine Transform ◽  
Compressed Images

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.

scholarly journals Image Compression System using ANN

2012 ◽  
pp. 20-24
Author(s):  
Disha Parkhi ◽  
S. S. Lokhande
Keyword(s):  
Image Compression ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
High Definition ◽  
Still Images ◽  
Cosine Transform ◽  
Compression System ◽  
High Definition Television ◽  
Compressed Images ◽  
The Neural Network

The rapid growth of digital imaging applications, including desktop publishing, multimedia, teleconferencing, and high definition television (HDTV) has increased the need for effective and standardized image compression techniques. Among the emerging standards are JPEG, for compression of still images; MPEG, for compression of motion video; and CCITT H.261 (also known as Px64), for compression of video telephony and teleconferencing. All three of these standards employ a basic technique known as the discrete cosine transform (DCT), Developed by Ahmed, Natarajan, and Rao [1974]. Image compression using Discrete Cosine Transform (DCT) is one of the simplest commonly used compression methods. The quality of compressed images, however, is marginally reduced at higher compression ratios due to the lossy nature of DCT compression, thus, the need for finding an optimum DCT compression ratio. An ideal image compression system must yield high quality compressed images with good compression ratio, while maintaining minimum time cost. The neural network associates the image intensity with its compression ratios in search for an optimum ratio.

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