Mrs. Poonam Dabas, Priya Soni / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.759-763 images, which can be thought of as a superposition of a much larger number of simple patterns. Can the models for the interactions between a few simple patterns generalize to evaluate interactions between tens or hundreds of patterns? Is this limited number of simple-stimulus experiments sufficient to build a model that can predict the visual quality of system which has an explanation planning component that uses a substantial domain knowledge base to construct answers to student queries in the domain of electrical circuits. These classifications are really points along a continuum, and serve as good rules of thumb rather than skill, and hence cognitive in nature. However, the emphasis of this system is also knowledge based and involves generating explanations and using general pedagogical tactics for generating feedback. Generally, tutors that teach procedural skills use a cognitive task analysis of expert behaviour, while tutors that teach concepts and frameworks use a larger knowledge base and place more emphasis on communication to be effective during instruction. II. NEW PHILOSHIPY In previous algorithm we have seen that this can calculate the quality of image. But in this case the MSE of all the images is almost same. But this new algorithm will calculate the quality of image and gives the best image quality. This will calculate the quality of image in the case of the distortion. This will calculate the quality of image in the noise. This algorithm the error in the quality of image if picture is not clear. These Results are in agreement with visual quality of the corresponding images. Although we cannot define some clear criterion for subjective quality assessment, human observer can intuitively feel when distorted image is more or less close to the reference image. Most human observers would agree with the rankings given by the proposed quality measure and in that sense these results are very reasonable. Steps for New Model:1. Given the reference image f(m,n), distorted image p(m,n), width, height and number of pixels of the input images and viewing distance, compute images x(m,n) and y(m,n) using the described model of HVS. 2. Compute the average correlation coefficient as the average value of locally computed correlation coefficients between images x(m,n) and y(m,n). 3. Compute the average correlation coefficient as the average value of locally computed correlation coefficients between images x(m,n) and e(m,n). 4. Compute Image Quality as MSE of frequency domain coefficients obtained after some transform (DFT, DCT etc) 5. Find the Image Quality Measure.
Lena Image with Various Type of Distortion:-
Fig. 2 Lena Image with Impulsive Salt Paper Noise
Fig. 3 Lena Image with Additive White Noise
Fig. 4 Lena Image with Blurring III. FUTURE WORK In this section, we are representing the only the measurement of image quality and we are discussing the various algorithms that represent the measurement of quality of image.
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