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.
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.
This study presents a methodology of forming contextually valid scales for subjective video quality measurement. Any
single value of quality e.g. Mean Opinion Score (MOS) can have multiple underlying causes. Hence this kind of a
quality measure is not enough for example, in describing the performance of a video capturing device. By applying
Interpretation Based Quality (IBQ) method as a qualitative/quantitative approach we have collected attributes familiar to the end user and that are extracted directly from the material offered by the observers' comments. Based on these
findings we formed contextually valid assessment scales from the typically used quality attributes. A large set of data
was collected from 138 observers to generate the video quality vocabulary. Video material was shot by three types of
video cameras: Digital video cameras (4), digital still cameras (9) and mobile phone cameras (9). From the quality
vocabulary, we formed 8 unipolar 11-point scales to get better insight of video quality. Viewing conditions were adjusted
to meet the ITU-T Rec. P.910 requirements. It is suggested that the applied qualitative/quantitative approach is especially
efficient for finding image quality differences in video material where the quality variations are multidimensional in
nature and especially when image quality is rather high.
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.
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 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.
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.
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