Objective image quality measurement by local spatial‐frequency wavelet analysis

An essential determinant of the value of surrogate digital images is their quality. Image quality measurement has become crucial for most image processing applications. Over the past years, there have been many attempts to develop models or metrics for image quality that incorporate elements of human visual sensitivity. However, there is no current standard and objective definition of spectral image quality. This paper proposes a reliable automatic method for objective image quality measurement by local spatial‐frequency wavelet analysis. The analysis is performed locally by dividing an image into 64×64 pixel blocks, and transforming the data into wavelet domain and sub‐band image domain. A fast lifting wavelet algorithm is developed for computationally efficient spatial‐frequency deconstruction of images to extract the features of edges in sub‐band datasets. Wavelet analysis throughout the spatial‐frequency range with respect to noise, sharpness, brightness, contrast, and modulation transfer function (MTF) reveals more detailed information to relate the quality of an image to the interpretation by using an artificial neural network to optimize weightings and results in a promising quantitative measure of image quality. Experimental results from using this method for image quality measurement exhibit good correlation to subjective visual quality assessments. The proposed approach with fast computation provides a potential metric for reliable image quality quantification, allowing image quality to be automatically evaluated following image capture/compression, but prior to permanent storage in a database.

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