Tero Vuori

Content and quality: Interpretation-based estimation of image quality

Test image contents affect subjective image-quality evaluations. Psychometric methods might show that contents have an influence on image quality, but they do not tell what this influence is like, i.e., how the contents influence image quality. To obtain a holistic description of subjective image quality, we have used an interpretation-based quality (IBQ) estimation approach, which combines qualitative and quantitative methodology. The method enables simultaneous examination of psychometric results and the subjective meanings related to the perceived image-quality changes. In this way, the relationship between subjective feature detection, subjective preferences, and interpretations are revealed. We report a study that shows that different impressions are conveyed in five test image contents after similar sharpness variations. Thirty naïve observers classified and freely described the images after which magnitude estimation was used to verify that they distinguished the changes in the images. The data suggest that in the case of high image quality, the test image selection is crucial. If subjective evaluation is limited only to technical defects in test images, important subjective information of image-quality experience is lost. The approach described here can be used to examine image quality and it will help image scientists to evaluate their test images.

Evaluating the multivariate visual quality performance of image-processing components

The estimation of image quality is a demanding task, especially when estimating different high-quality imaging products or their components. The challenge is the multivariate nature of image quality as well as the need to use naïve observers as test subjects, since they are the actual end-users of the products. Here, we use a subjective approach suitable for estimating the quality performance of different imaging device components with naïve observers—the interpretation-based quality (IBQ) approach. From two studies with 61 naïve observers, 17 natural image contents, and 13 different camera image signal processor pipelines, we determined the subjectively crucial image quality attributes and dimensions and the description of each pipelines perceived image quality performance. We found that the subjectively most important image quality dimensions were color shift/naturalness, darkness, and sharpness. The first dimension, which was related to naturalness and colors, distinguished the good-quality pipelines from the middle- and low-quality groups, and the dimensions of darkness and sharpness described why the quality failed in the low-quality pipelines. The study suggests that the high-level concept naturalness is a requirement for high-quality images, whereas quality can fail for other reasons in low-quality images, and this failure can be described by low-level concepts, such as darkness and sharpness.

What do users really perceive - : probing the subjective image quality

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 observers 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.

Evaluation of the visual performance of image processing pipes: information value of subjective image attributes

Subjective image quality data for 9 image processing pipes and 8 image contents (taken with mobile phone camera, 72 natural scene test images altogether) from 14 test subjects were collected. A triplet comparison setup and a hybrid qualitative/quantitative methodology were applied. MOS data and spontaneous, subjective image quality attributes to each test image were recorded. The use of positive and negative image quality attributes by the experimental subjects suggested a significant difference between the subjective spaces of low and high image quality. The robustness of the attribute data was shown by correlating DMOS data of the test images against their corresponding, average subjective attribute vector length data. The findings demonstrate the information value of spontaneous, subjective image quality attributes in evaluating image quality at variable quality levels. We discuss the implications of these findings for the development of sensitive performance measures and methods in profiling image processing systems and their components, especially at high image quality levels.

Nokia PureView oversampling technology

This paper describes Nokia’s PureView oversampling imaging technology as well as the product, Nokia 808 PureView, featuring it. The Nokia PureView imaging technology is the combination of a large, super high resolution 41Mpix with high performance Carl Zeiss optics. Large sensor enables a pixel oversampling technique that reduces an image taken at full resolution into a lower resolution picture, thus achieving higher definition and light sensitivity. One oversampled super pixel in image file is formed by using many sensor pixels. A large sensor enables also a lossless zoom. If a user wants to use the lossless zoom, the sensor image is cropped. However, up-scaling is not needed as in traditional digital zooming usually used in mobile devices. Lossless zooming means image quality that does not have the digital zooming artifacts as well as no optical zooming artifacts like zoom lens system distortions. Zooming with PureView is also completely silent. PureView imaging technology is the result of many years of research and development and the tangible fruits of this work are exceptional image quality, lossless zoom, and superior low light performance.

Measuring multivariate subjective image quality for still and video cameras and image processing system components

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.

Threshold value for acceptable video quality using signal-to-noise ratio

Noise decreases video quality considerably, particularly in dark environments. In a video clip, noise can be seen as an unwanted spatial or temporal variation in pixel values. The object of the study was to find a threshold value for signal-to-noise ratio (SNR) in which the video quality is perceived to be good enough. Different illumination levels for video shooting were studied using both subjective and objective (SNR measurements) methodologies. Five camcorders were selected to cover different sensor technologies, recording formats and price categories. The test material for the subjective test was recorded in an environment simulator, where it was possible to adjust lighting levels. Double staircase test was used as the subjective test method. The test videos for objective measurements were recorded using an ISO 15739 based environment. There was a correlation found between objective and subjective measurements, between measured SNR and perceived quality. Good enough video quality was reached between SNR values of 15.3 dB and 17.2 dB. With 3CCD and super HAD-CCD technologies, video quality was brighter, less noisy, and the SNR was better in low light conditions compared to the quality with conventional CCDs.

The effect of image sharpness on quantitative eye movement data and on image quality evaluation while viewing natural images

The aim of the study is to test both customer image quality rating (subjective image quality) and physical measurement of user behavior (eye movements tracking) to find customer satisfaction differences in imaging technologies. Methodological aim is to find out whether eye movements could be quantitatively used in image quality preference studies. In general, we want to map objective or physically measurable image quality to subjective evaluations and eye movement data. We conducted a series of image quality tests, in which the test subjects evaluated image quality while we recorded their eye movements. Results show that eye movement parameters consistently change according to the instructions given to the user, and according to physical image quality, e.g. saccade duration increased with increasing blur. Results indicate that eye movement tracking could be used to differentiate image quality evaluation strategies that the users have. Results also show that eye movements would help mapping between technological and subjective image quality. Furthermore, these results give some empirical emphasis to top-down perception processes in image quality perception and evaluation by showing differences between perceptual processes in situations when cognitive task varies.

P‐28: Image Adjustment with the Tone Rendering Curve

This paper investigates the suitability of different tone rendering curves (gamma functions) at different external illumination levels and with different display content. It is difficult to find an optimum gamma value because image quality strongly depends on the image content and the external illumination. Tuning the tone rendering curve based on ambient light sensor and / or image content of the display would significantly increase the perceived image quality.

Imaging lens design using image quality metric

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.