Analysis of JPEG Digital Image Compression Process
JPEG is the most often used image compression standard that is used since 1992. It is a lossy compression method, and is widely used in digital cameras and mobile phones. Depending on the parameters and user needs, it can achieve a compression ratio between 10 and 50. Memory for digital image storage is saved on the expense of decompressed image quality. The method is based on the Discrete Cosine Transform (DCT) that separates the image into its different frequency components. This paper shows how different parameters of the algorithm influence the performance of the compression. In the end, ideas are given how to either increase the compression ratio keeping the same decompressed image quality, or to improve the quality without decreasing the compression ratio. The quality between the original and the decompressed images is measured using two objective criteria: the Peak Signal-to-Noise Ratio (PSNR) and the structural similarity index (SSIM).
Joint Photographic Expert Group (JPEG). Information technology – digital compression and coding of continuous-tone still images – part 1: requirements and guidelines. ISO/IEC 10918-1, ITU/CCITT Rec. T.81, 1992.
N. Ahmed, T. Natarajan, K. R. Rao: Discrete Cosine Transform, IEEE Trans. on Computers, 23, pp. 90-93, 1974., DOI: 10.1109/T-C.1974.223784.
G. K. Wallace: The JPEG still picture compression standard, IEEE Transactions on Consumer Electronics, 38(1), 1992., DOI:10.1109/30.125072.
Thai, Cogranne, Retraint: JPEG Quantization Step Estimation and Its Applications to Digital Image Forensics, IEEE Transactions on Information Forensics and Secutity, Vol. 12, No. 1, 2017., pp. 123-133., DOI: 10.1109/TIFS.2016.2604208.
Wang, Lee, Chang: Designing JPEG quantization tables based on human visual system, Elsevier, 2001, signal Processing: Image communication 16, pp. 501-506., DOI:10.1109/ICIP.1999.82921.
Tan, Gan: Perceptual Image Coding with Discrete Cosine Transform, Springer Briefs in Electrical and Computer Engineering, 2015., DOI: 10.1007/978-981-287-543-3.
K. S. Thyagarajan: Still Image and Video Compression with Matlab, John Wiley & Sons, 2011, ISBN 978-0-47048416-6.
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