The subjective quality of an image is a non-linear product of several, simultaneously contributing subjective factors such
as the experienced naturalness, colorfulness, lightness, and clarity. We have studied subjective image quality by using a
hybrid qualitative/quantitative method in order to disclose relevant attributes to experienced image quality. We describe
our approach in mapping the image quality attribute space in three cases: still studio image, video clips of a talking head
and moving objects, and in the use of image processing pipes for 15 still image contents. Naive observers participated in
three image quality research contexts in which they were asked to freely and spontaneously describe the quality of the
presented test images. Standard viewing conditions were used. The data shows which attributes are most relevant for
each test context, and how they differentiate between the selected image contents and processing systems. The role of
non-HVS based image quality analysis is discussed.
We present an effective method for comparing subjective audiovisual quality and the features related to the quality
changes of different video cameras. Both quantitative estimation of overall quality and qualitative description of critical
quality features are achieved by the method. The aim was to combine two image quality evaluation methods, the
quantitative Absolute Category Rating (ACR) method with hidden reference removal and the qualitative Interpretation-
Based Quality (IBQ) method in order to see how they complement each other in audiovisual quality estimation tasks. 26
observers estimated the audiovisual quality of six different cameras, mainly mobile phone video cameras. In order to
achieve an efficient subjective estimation of audiovisual quality, only two contents with different quality requirements
were recorded with each camera. The results show that the subjectively important quality features were more related to
the overall estimations of cameras' visual video quality than to the features related to sound. The data demonstrated two
significant quality dimensions related to visual quality: darkness and sharpness. We conclude that the qualitative
methodology can complement quantitative quality estimations also with audiovisual material. The IBQ approach is
valuable especially, when the induced quality changes are multidimensional.
Image evaluation schemes must fulfill both objective and subjective requirements. Objective image quality evaluation models are often preferred over subjective quality evaluation, because of their fastness and cost-effectiveness. However, the correlation between subjective and objective estimations is often poor. One of the key reasons for this is that it is not known what image features subjects use when they evaluate image quality. We have studied subjective image quality evaluation in the case of image sharpness. We used an Interpretation-based Quality (IBQ) approach, which combines both qualitative and quantitative approaches to probe the observer's quality experience. Here we examine how naive subjects experienced and classified natural images, whose sharpness was changing. Together the psychometric and qualitative information obtained allows the correlation of quantitative evaluation data with its underlying subjective attribute sets. This offers guidelines to product designers and developers who are responsible for image quality. Combining these methods makes the end-user experience approachable and offers new ways to improve objective image quality evaluation schemes.
In the case of imaging optics for imaging cellular phones, special attention has to be paid on the cost of the lens system. The number of lens elements has to be minimized, but the image quality has to be maximized. It is important that optimum quality/cost - ratio is found. The image sensor characteristics and human visual system preferences have to be taken into consideration as well for the design. In this paper, we present our new image quality metric. The performance of the metric is investigated using subjective tests on different lens designs and compared with MTF metric. We show that our metric has a good correlation with human observer and performs better than MTF metric. Finally, we give some examples of optimization based on our metric.
We propose a new method to improve the design of electro- optical imaging system using an end-to-end model of the imaging systems and a combination of image quality criteria. Firstly, we used an imaging systems simulator to produce an output image, which is the distorted version of the input scene. Secondly, we calculate an objective quotation for the quality of the output images corresponding to several systems configurations, and compare the results with the human evaluation of image quality. It allows us to calibrate our imaging systems quality measure (ISQM). Finally, the ISQM is used as a tool to improve system design without any human observer evaluation.
We propose a new method to improve the design of electro- optical imaging system using an end-to-end model of the imaging system and a combination of image quality criteria. Firstly, we used an imaging system simulator to produce an output image, which is the distorted version of the input scene. Secondly, we calculate an objective quotation for the quality of the imaging system for each parameter set, and compare the results with the human evaluation of image quality. It allows us to calibrate our imaging system quality measure (ISQM). Finally, the ISQM is used as a tool to improve system design without any human observer evaluation.