Analyzing the Effect of JPEG Compression on Local Variance of Image Intensity

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.

[1]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Kim-Han Thung,et al.  Content-based image quality metric using similarity measure of moment vectors , 2012, Pattern Recognit..

[3]  Alan C. Bovik,et al.  DCT-domain blind measurement of blocking artifacts in DCT-coded images , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[4]  Ling Guan,et al.  Adaptive Image Processing: A Computational Intelligence Perspective , 2001 .

[5]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[6]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[7]  P. K. Bocher,et al.  The fundamentals of average local variance - part I: detecting regular patterns , 2006, IEEE Transactions on Image Processing.

[8]  Tiago Rosa Maria Paula Queluz,et al.  No-reference image quality assessment based on DCT domain statistics , 2008, Signal Process..

[9]  Sam Kwong,et al.  Novel variance based approach to improving JPEG decoding , 2005, 2005 IEEE International Conference on Industrial Technology.

[10]  Sanjit K. Mitra,et al.  Enhancement of Color Images by Scaling the DCT Coefficients , 2008, IEEE Transactions on Image Processing.

[11]  F. Muller.  Distribution shape of two-dimensional DCT coefficients of natural images , 1993 .

[12]  Joseph W. Goodman,et al.  A mathematical analysis of the DCT coefficient distributions for images , 2000, IEEE Trans. Image Process..

[13]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[14]  Ioannis Pratikakis,et al.  Adaptive degraded document image binarization , 2006, Pattern Recognit..

[15]  Søren Forchhammer,et al.  Adaptive deblocking and deringing of H.264/AVC video sequences , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[17]  Aimin Zhou,et al.  Adaptive image segmentation by using mean-shift and evolutionary optimisation , 2014, IET Image Process..

[18]  Patrenahalli M. Narendra,et al.  Real-Time Adaptive Contrast Enhancement , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  C. Lin,et al.  Histogram-oriented watermarking algorithm: colour image watermarking scheme robust against geometric attacks and signal processing , 2006 .

[20]  J. Alex Stark,et al.  Adaptive image contrast enhancement using generalizations of histogram equalization , 2000, IEEE Trans. Image Process..

[21]  Anastasios N. Venetsanopoulos,et al.  A perceptual model for JPEG applications based on block classification, texture masking, and luminance masking , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[22]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[23]  Majid Rabbani,et al.  Biased reconstruction for JPEG decoding , 1999, IEEE Signal Processing Letters.

[24]  Samuel Kotz,et al.  On the DCT Coefficient Distributions , 2006, IEEE Signal Processing Letters.

[25]  Gaobo Yang,et al.  Referenceless Measure of Blocking Artifacts by Tchebichef Kernel Analysis , 2014, IEEE Signal Processing Letters.

[26]  André Kaup,et al.  A no-reference Blocking Artifacts Visibility Estimator in images , 2010, 2010 IEEE International Conference on Image Processing.

[27]  Taejeong Kim,et al.  Regression-based prediction for blocking artifact reduction in JPEG-compressed images , 2005, IEEE Transactions on Image Processing.

[28]  Qionghai Dai,et al.  Stereo Interleaving Video Coding With Content Adaptive Image Subsampling , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Fernando Pérez-González,et al.  Skewed log-stable model for natural images pixel block-variance , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[30]  Héctor M. Pérez Meana,et al.  Robust watermarking based on histogram modification , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[31]  Gopal Thirumalai Narayanan,et al.  A study of probability distributions of DCT coefficients in JPEG compression , 2010 .

[32]  Gang Xiong,et al.  Steganalysis of LSB matching based on local variance histogram , 2010, 2010 IEEE International Conference on Image Processing.

[33]  Wen-Rong Wu,et al.  Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation , 1998, IEEE Trans. Medical Imaging.

[34]  Jong-Ki Han,et al.  PSNR estimation scheme using coefficient distribution of frequency domain in H.264 decoder , 2008 .

[35]  S. Liu,et al.  Statistical analysis of the DCT coefficients and their quantization error , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.