NDLI: Statistical characterisation of block variance and AC DCT coefficients for power law enhanced images

Content Provider	IET Digital Library
Author	Singh, Neetu Gupta, Abhinav Jain, Roop C.
Abstract	The study of statistical distributions of alternating current (AC) discrete cosine transform (DCT) coefficients is one of the key techniques for digital images. In this study, we have analysed original and power law enhanced images in the logarithmic domain. The logarithmic domain linearises the otherwise nonlinear relation between original and power law enhanced images. We have experimentally proved that Gamma distribution is the best distribution for characterisation of block variance in terms of Jensen–Shannon divergence. Therefore, a composite distribution, Gaussian-Gamma, is employed for characterisation of AC DCT coefficients. We have analytically derived and experimentally verified that the scale parameters of power law enhanced image are proportional to scale parameters of the original image whereas shape parameters remain unchanged. On further experimentation, it is established that scale and shape parameters of the composite statistical distribution of AC DCT coefficients do not change if images are compressed in JPEG format after power law enhancement. Furthermore, a novel feature set of scale parameters is constructed and is applied to train decision tree to classify original, brightened, and darkened images. The comparison of achieved classification results with the state-of-the-art show the efficacy of proposed analysis.
Starting Page	2128
Ending Page	2137
Page Count	10
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5596
Journal	IET Image Processing
Publisher Date	2018-08-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	(DCT) Coefficients AC DCT Coefficient Block Variance Composite Distribution Composite Statistical Distribution Computer Vision And Image Processing Technique Current Discrete Cosine Decision Tree Digital Image Discrete Cosine Transform Gamma Distribution Gaussian Distribution Image Coding Integral Transforms Logarithmic Domain Linearises Optical, Image And Video Signal Processing Original Image Power Law Enhancement Scale Parameter Shape Parameter Statistical Characterisation Statistical Distribution Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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