This investigation examines the relationships between image fidelity, acceptability thresholds and scene content for images distorted by lossy compression. <i>Scene characteristics</i> of a sample set of images, with a wide range of representative scene content, were quantified, using simple measures (<i>scene metrics</i>), which had been previously found to correlate with global scene lightness, global contrast, busyness, and colorfulness. Images were compressed using the lossy JPEG 2000 algorithm to a range of compression ratios, progressively introducing distortion to levels beyond the threshold of detection. Twelve observers took part in a paired comparison experiment to evaluate the perceptibility threshold compression ratio. A further psychophysical experiment was conducted using the same scenes, compressed to higher compression ratios, to identify the level of compression at which the images became visually unacceptable. Perceptibility and acceptability thresholds were significantly correlated for the test image set; both thresholds also correlated with the busyness metric. Images were ranked for the two thresholds and were further grouped, based upon the relationships between perceptibility and acceptability. Scene content and the results from the scene descriptors were examined within the groups to determine the influence of specific common scene characteristics upon both thresholds.
Sorting and searching operations used for the selection of test images strongly affect the results of image quality
investigations and require a high level of versatility. This paper describes the way that inherent image properties, which
are known to have a visual impact on the observer, can be used to provide support and an innovative answer to image
selection and classification. The selected image properties are intended to be comprehensive and to correlate with our
perception. Results from this work aim to lead to the definition of a set of universal scales of perceived image properties
that are relevant to image quality assessments.
The initial prototype built towards these objectives relies on global analysis of low-level image features. A
multidimensional system is built, based upon the global image features of: lightness, contrast, colorfulness, color
contrast, dominant hue(s) and busyness. The resulting feature metric values are compared against outcomes from
relevant psychophysical investigations to evaluate the success of the employed algorithms in deriving image features that
affect the perceived impression of the images.
The paper is focused on the implementation of a modular color image difference model, as described in , with aim to predict visual magnitudes between pairs of uncompressed images and images compressed using lossy JPEG and JPEG 2000. The work involved programming each pre-processing step, processing each image file and deriving the error map, which was further reduced to a single metric. Three contrast sensitivity function implementations were tested; a
Laplacian filter was implemented for spatial localization and the contrast masked-based local contrast enhancement method, suggested by Moroney, was used for local contrast detection. The error map was derived using the CIEDE2000 color difference formula on a
pixel-by-pixel basis. A final single value was obtained by calculating the median value of the error map. This metric was finally tested against relative quality differences between original and compressed images, derived from psychophysical investigations on the same dataset. The outcomes revealed a grouping of images which was attributed to correlations between the <i>busyness</i> of the test scenes (defined as image property indicating the presence or absence of high frequencies) and different clustered results. In conclusion, a method for accounting for the amount of detail in test is required for a more accurate prediction of image quality.