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This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field. The parameters of the model are estimated based on Bayesian approach. The advantage of the proposed model is that it adapts itself according to the nature of the data (image) because it has infinite structure with a finite number of parameters, and so completely avoids the problem of order determination. The proposed model is fitted to reconstruct the image with the use of estimated parameters and seed values. The residual image is computed from the original and the reconstructed images. The proposed FRGMRF model is redefined as an error model to compress the residual image to obtain better quality of the reconstructed image. The parameters of the error model are estimated by employing the Metropolis-Hastings (M-H) algorithm. Then, the error model is fitted to reconstruct the compressed residual image. The Arithmetic coding is employed on seed values, average of the residuals and the model coefficients of both the input and residual images to achieve higher compression ratio. Different types of textured and structured images are considered for experiment to illustrate the efficiency of the proposed model. The results obtained by the FRGMRF model are compared to the JPEG2000. The proposed approach yields higher compression ratio than the JPEG whereas it produces Peak Signal to Noise Ratio (PSNR) with little higher than the JPEG, which is negligible.
The techniques of test
case prioritization schedule the execution order of test cases to attain
respective target, such as enhanced level of forecasting the fault. The
requirement of the prioritization can be viewed as the en-route for deriving an
order of relation on a given set of test cases which results from regression
testing. Alteration of programs between the versions can cause more test cases
which may respond differently to following versions of software. In this, a
fixed approach to prioritizing test cases avoids the preceding drawbacks. The
JUnit test case prioritization techniques operating in the absence of coverage
information, differs from existing dynamic coverage-based test case
prioritization techniques. Further, the prioritization test cases relying on
coverage information were projected from fixed structures relatively other than
gathered instrumentation and execution.