Abstract: Content-based image retrieval (CBIR) uses the
contents of images to characterize and contact the images. This paper
focus on retrieving the image by separating images into its three color
mechanism R, G and B and for that Discrete Wavelet Transformation
is applied. Then Wavelet based Generalized Gaussian Density (GGD)
is practical which is used for modeling the coefficients from the
wavelet transforms. After that it is agreed to Histogram of Oriented
Gradient (HOG) for extracting its characteristic vectors with Relevant
Feedback technique is used. The performance of this approach is
calculated by exactness and it confirms that this method is wellorganized
for image retrieval.
Abstract: Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.
Abstract: In This Article We establish moment inequality of
dependent random variables,furthermore some theorems of strong law
of large numbers and complete convergence for sequences of dependent
random variables. In particular, independent and identically
distributed Marcinkiewicz Law of large numbers are generalized to
the case of m0-dependent sequences.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.