Abstract
The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition. It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression. Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method. The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database-Brodatz Textures. Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy.
Highlights
Development of objective image quality assessment methods in recent years have caused some new possibilities of their usage in a wide area of applications.Krzysztof Okarma, Paweł ForczmańskiThe traditionally used full-reference image quality metrics, such as Mean Square Error, Peak Signal to Noise Ratio etc., require the usage of original image for comparison with the distorted one being assessed
For many applications it is an acceptable approach, in some cases the additional information about the quality of the image can be even included in the image file for utilization if the original image is unknown for the end-user
The average value of SSIM from the whole quality map is treated as the overall image quality metric, which is sensitive to three common types of distortions introduced by the image processing algorithms: loss of correlation, luminance distortion and loss of contrast
Summary
Development of objective image quality assessment methods in recent years have caused some new possibilities of their usage in a wide area of applications. The average value of SSIM from the whole quality map is treated as the overall image quality metric, which is sensitive to three common types of distortions introduced by the image processing algorithms: loss of correlation, luminance distortion and loss of contrast Another idea, presented in [14] is using Singular Value Decomposition (SVD) applied for 8×8 pixels blocks of original and distorted images to compare them. C tests where the observers evaluate the quality of presented images filling up the questionnaires and their statistical analysis is performed They can be useful for the development of some new objective metrics, even better correlated with HVS, but cannot be directly used in computer applications where results should be calculated in the short time without any human interactions. In some systems the additional estimation of the “differential” image quality metric for two neighboring video frames is possible e.g. for detection of rapid changes of lighting conditions, image sharpness etc. and their influence on the recognition accuracy
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