18 January 2010 Validating a texture metric for camera phone images using a texture-based softcopy attribute ruler
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Abstract
Imaging systems in camera phones have image quality limitations attributed to optics, size, and cost constraints. These limitations generally result in unwanted system noise. In order to minimize the image quality degradation, nonlinear noise cleaning algorithms are often applied to the images. However, as the strength of the noise cleaning increases, this often leads to texture degradation. The Camera Phone Image Quality (CPIQ) initiative of the International Imaging Industry Association (I3A) has been developing metrics to quantify texture appearance in camera phone images. Initial research established high correlation levels between the metrics and psychophysical data from sets of images that had noise cleaning filtering applied to simulate capture in actual camera phone systems. This paper describes the subsequent work to develop a texture-based softcopy attribute ruler in order to assess the texture appearance of eight camera phone units from four different manufacturers and to assess the efficacy of the texture metrics. Multiple companies participating in the initiative have been using the softcopy ruler approach in order to pool observers and increase statistical significance. Results and conclusions based on three captured scenes and two texture metrics will be presented.
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Jonathan B. Phillips, Douglas Christoffel, "Validating a texture metric for camera phone images using a texture-based softcopy attribute ruler", Proc. SPIE 7529, Image Quality and System Performance VII, 752904 (18 January 2010); doi: 10.1117/12.839108; https://doi.org/10.1117/12.839108
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