Image-quality assessment measures are largely based on the assumption that an image is only distorted by one type of distortion at a time. These conventional measures perform poorly if an image includes more than one distortion. In consumer photography, captured images are subject to many sources of distortions and modifications. We searched for feature subsets that predict the quality of photographs captured by different consumer cameras. For this, we used the new CID2013 image database, which includes photographs captured by a large number of consumer cameras. Principal component analysis showed that the features classified consumer camera images in terms of sharpness and noise energy. The sharpness dimension included lightness, detail reproduction, and contrast. The support vector regression model with the found feature subset predicted human observations well compared to state-of-the-art measures.
Depth perception is an important component of many augmented reality applications. It is, however, subject to multiple error sources. In this study, we investigated depth judgments with a stereoscopic video see-through head-mounted display for the purpose of designing depth cueing for systems that operate in an individual’s action space. In the experiment, we studied the use of binocular disparity and relative size to improve relative depth judgments of augmented objects above the ground plane. The relative size cue was created by adding auxiliary augmentations to the scene according to constraints described in the section on the underlying theory. The results showed that binocular disparity and relative size improved depth judgments over the distance range. This indicates that for accurate depth judgments, additional depth cues should be used to facilitate stereoscopic perception within an individual’s action space.
This study presents a geometric and subjective analysis of typical mobile stereoscopic 3-D images. The geometry of the stereoscopic pipeline from the scene to the eyes of the viewer is a highly relevant issue in stereoscopic media. One important factor is camera separation, because it can be used to control the perceived depth of stereoscopic images. The geometric analysis included consideration of disparity and roundness factor within typical mobile stereoscopic imaging scenes. These geometric properties of stereoscopic 3-D images were compared to subjective evaluations by varying camera separation in different scenes. The participants in this study evaluated the strength and naturalness of depth sensation and the overall viewing experience from still images with the single-stimulus method. The results showed that participants were able to perceive the change of depth range even though the images were shown in random order without a reference depth scale. The highest naturalness of depth sensation and viewing experience were achieved with 2 cm to 6 cm camera separation in every content. With these preferred camera separations, the disparity range was less than ±1 deg and cardboard effect (quantified with roundness factor) did not negatively affect the naturalness of depth sensation.
Camera separation affects the perceived depth in stereoscopic movies. Through control of the separation and thereby the
depth magnitudes, the movie can be kept comfortable but interesting. In addition, the viewing context has a significant
effect on the perceived depth, as a larger display and longer viewing distances also contribute to an increase in depth.
Thus, if the content is to be viewed in multiple viewing contexts, the depth magnitudes should be carefully planned so
that the content always looks acceptable. Alternatively, the content can be modified for each viewing situation. To
identify the significance of changes due to the viewing context, we studied the effect of stereoscopic camera base
distance on the viewer experience in three different situations: 1) small sized video and a viewing distance of 38 cm, 2)
television and a viewing distance of 158 cm, and 3) cinema and a viewing distance of 6-19 meters. We examined three
different animations with positive parallax. The results showed that the camera distance had a significant effect on the
viewing experience in small display/short viewing distance situations, in which the experience ratings increased until the
maximum disparity in the scene was 0.34 - 0.45 degrees of visual angle. After 0.45 degrees, increasing the depth
magnitude did not affect the experienced quality ratings. Interestingly, changes in the camera distance did not affect the
experience ratings in the case of television or cinema if the depth magnitudes were below one degree of visual angle.
When the depth was greater than one degree, the experience ratings began to drop significantly. These results indicate
that depth magnitudes have a larger effect on the viewing experience with a small display. When a stereoscopic movie is
viewed from a larger display, other experiences might override the effect of depth magnitudes.
The added value of stereoscopy is an important factor for stereoscopic product development and content production.
Previous studies have shown that 'image quality' does not encompass the added value of stereoscopy, and thus the
attributes naturalness and viewing experience have been used to evaluate stereoscopic content. The objective of this
study was to explore what the added value of stereoscopy may consist of and what are the content properties that
contribute to the magnitude of the added value. The hypothesis was that interestingness is a significant component of the
added value. A subjective study was conducted where the participants evaluated three attributes of the stimuli in the
consumer photography domain: viewing experience, naturalness of depth and interestingness. In addition to the no-reference
direct scaling method a novel method, the recalled attention map, was introduced and used to study attention in
stereoscopic images. In the second part of our study, we use eye tracking to compare the salient regions in monoscopic
and stereoscopic conditions. We conclude from the subjective results that viewing experience and naturalness of depth
do not cover the entire added value of stereoscopy, and that interestingness brings a new dimension into the added value
research. The eye tracking data analysis revealed that the fixation maps are more consistent between participants in
stereoscopic viewing than in monoscopic viewing and from this we conclude that stereoscopic imagery is more effective
in directing the viewer's attention.
We present a method to evaluate stereo camera depth accuracy in human centered applications. It enables the comparison
between stereo camera depth resolution and human depth resolution. Our method uses a multilevel test target which can
be easily assembled and used in various studies. Binocular disparity enables humans to perceive relative depths
accurately, making a multilevel test target applicable for evaluating the stereo camera depth accuracy when the accuracy
requirements come from stereoscopic vision.
The method for measuring stereo camera depth accuracy was validated with a stereo camera built of two SLRs (singlelens
reflex). The depth resolution of the SLRs was better than normal stereo acuity at all measured distances ranging
from 0.7 m to 5.8 m. The method was used to evaluate the accuracy of a lower quality stereo camera. Two parameters,
focal length and baseline, were varied. Focal length had a larger effect on stereo camera's depth accuracy than baseline.
The tests showed that normal stereo acuity was achieved only using a tele lens.
However, a user's depth resolution in a video see-through system differs from direct naked eye viewing. The same test
target was used to evaluate this by mixing the levels of the test target randomly and asking users to sort the levels
according to their depth. The comparison between stereo camera depth resolution and perceived depth resolution was
done by calculating maximum erroneous classification of levels.
High dynamic range (HDR) imaging seems to have developed to a level of soon being a standard feature in
consumer cameras. This study was motivated by the need for evaluating tone mapping operators especially
for consumer imaging applications. A no-reference method based on ISO 20462-2:2005 triplet comparison was
created for evaluating tone mapping operators. Multiple HDR test images were photographed and the method
was validated by evaluating 25 tone mapping operators with five test images. Tone mapping operators were
evaluated based on image naturalness and pleasantness. The results indicate that the method successfully ranked
the method in terms of naturalness and pleasantness. The test image set could be improved for example based
on an imaging photo space for HDR photography. The test images of this study are available for non-commercial
Balanced and representative test images are needed to study perceived visual quality in various application domains.
This study investigates naturalness and interestingness as image quality attributes in the context of test images. Taking a
top-down approach we aim to find the dimensions which constitute naturalness and interestingness in test images and the
relationship between these high-level quality attributes. We compare existing collections of test images (e.g. Sony sRGB
images, ISO 12640 images, Kodak images, Nokia images and test images developed within our group) in an experiment
combining quality sorting and structured interviews. Based on the data gathered we analyze the viewer-supplied criteria
for naturalness and interestingness across image types, quality levels and judges. This study advances our understanding
of subjective image quality criteria and enables the validation of current test images, furthering their development.
Face detection techniques are used for many different applications. For example, face detection is a basic component in
many consumer still and video cameras. In this study, we compare the performance of face area data and freely selected
local area data for predicting the sharpness of photographs. The local values were collected systematically from images,
and for the analyses we selected only the values with the highest performance. The objective sharpness metric was based
on the statistics of the wavelet coefficients for the selected areas. We used three image contents whose subjective
sharpness values had been measured. The image contents were captured by 13 cameras, and the images were evaluated
by 25 subjects. The quality of the cameras ranged from low-end mobile phone cameras to low-end compact cameras.
The image contents simulated typical photos that consumers take with their mobile phones. The face area sizes on the
images were approximately 0.4, 1.0 or 4.0 %. Based on the results, the face area data proved to be valuable for
measuring the sharpness of the photographs if the face size was large enough. When the face area size was 1.0 or 4.0 %,
the performance of the measured sharpness values was equal to or better than the sharpness values measured from the
best local areas. When the face area was too small (0.4 %), the performance was low compared with the best local areas.
Objective image quality metrics can be based on test targets or algorithms. Traditionally, the image quality of digital
cameras has been measured using test targets. Test-target measurements are tedious and require a controlled laboratory
environment. Algorithm metrics can be divided into three groups: full-reference (FR), reduced-reference (RR) and noreference
(NR). FR metrics cannot be applied to the computation of image quality captured by digital cameras because
pixel-wise reference images are missing. NR metrics are applicable only when the distortion type is known and the
distortion space is low-dimensional. RR metrics provide a tradeoff between NR and FR metrics. An RR metric does not
require a pixel-wise reference image; it only requires a set of extracted features. With the aid of RR features, it is
possible to avoid problems related to NR metrics. In this study, we evaluate the applicability of RR metrics to measuring
the image quality of natural images captured by digital cameras. We propose a method in which reference images are
captured using a reference camera. The reference images represented natural reproductions of the views under study. We
tested our method using three RR metrics proposed in the literature. The results suggest that the proposed method is
promising for measuring the quality of natural images captured by digital cameras for the purpose of camera
The aim of the study was to develop a test image for print quality evaluation to improve the current state of the art in
testing the quality of digital printing. The image presented by the authors in EI09 portrayed a breakfast scene, the content
of which could roughly be divided in four object categories: a woman, a table with objects, a landscape picture and a
gray wall. The image was considered to have four main areas of improvement: the busyness of the image, the control of
the color world, the salience of the object categories, and the naturalness of the event and the setting. To improve the first
image, another test image was developed. Whereas several aspects were improved, the shortcomings of the new image
found by visual testing and self-report were in the same four areas. To combine the insights of the two test images and to
avoid their pitfalls, a third image was developed. The goodness of the three test images was measured in subjective tests.
The third test image was found to address efficiently three of the four improvement areas, only the salience of the objects
left a bit to be desired.
The goal of the study was to develop a method for quality computation of digitally printed images. We wanted to use
only the attributes which have a meaning for subjective visual quality experience of digitally printed images. Based on
the subjective data and our assessments the attributes for quality calculation were sharpness, graininess and color
contrast. The proposed graininess metric divides the fine detail image into blocks and used the low energy blocks for
graininess calculation. The proposed color contrast metric computes the contrast of dominant colors using the coarse
scale image. The proposed sharpness metric divides the coarse scale image into blocks and uses the high energy blocks
for sharpness calculation. The reduced reference features of sharpness and graininess metrics are the numbers of high or
low energy blocks. The reduced reference features of the color contrast metric are the directions of the dominant colors
in reference image. The overall image quality was calculated by combining the values. The performance of proposed
application specific image quality metric was high compared to the state of the art reduced reference applicationindependent
image quality metric. Linear correlation coefficients between subjective and predicted MOS were 0.88 for
electrophotography and 0.98 for ink-jet printed samples, for a sample set of 21 prints for electrophotography and for inkjet,
subjectively evaluated by 28 observers.
Digital cameras, printers and displays have their own established methods to measure their performance. Different
devices have their own special features and also different metrics and measuring methods. The real meaning of
measuring data is often not learnt until hands-on experience is available. The goal of this study was to describe a
preliminary method and metrics for measuring the objective image quality of the TV-out function of mobile handsets.
The TV-out application was image browsing.
Image quality is often measured in terms of color reproduction, noise and sharpness and these attributes were also
applied in this study. The color reproduction attribute was studied with color depth, hue reproduction and color accuracy
metrics. The noise attribute was studied with the SNR (signal to noise ratio) and chroma noise metrics. The sharpness
attribute was studied with the SFR (spatial frequency response) and contrast modulation metrics. The measuring data
was gathered by using a method which digitized the analog signal of the TV-out device with a frame grabber card.
Based on the results, the quantization accuracy, chroma error and spatial reproduction of the signal were the three
fundamental factors which most strongly affected the performance of the TV-out device. The quantization accuracy of
the device affects the number of tones that can be reproduced in the image. The quantization accuracy also strongly
affects the correctness of hue reproduction. According to the results, the color depth metric was a good indicator of
quantization accuracy. The composite signal of TV-out devices transmits both chroma and luminance information in a
single signal. A change in the luminance value can change the constant chroma value. Based on the results, the chroma
noise metric was a good indicator for measuring this phenomenon. There were differences between the spatial
reproductions of the devices studied. The contrast modulation was a clear metric for measuring these differences. The
signal sharpening of some TV-out devices hindered the interpretation of SFR data.
A test image for color still image processes was developed. The image is based on general requirements on the content
and specific requirements arising from the quality attributes of interest. The quality attributes addressed in the study
include sharpness, noise, contrast, colorfulness and gloss. These were chosen based on visual relevance in studies of the
influence of paper in digital printing. Further requirements such as arising from the use cases of the image are discussed
based on eye tracking data and self-report of the usefulness of different objects for quality evaluation. From the
standpoint of being sufficiently sensitive to quality variations of the imaging systems to be measured the reference test
image needs to represent quality maxima in terms of the relevant quality parameters. As for different viewing times, no
object should be exceedingly salient. The paper presents the procedure of developing the test image and discusses its
merits and shortcomings from the standpoint of future development.
The psychological complexity of multivariate image quality evaluation makes it difficult to develop general image quality metrics. Quality evaluation includes several mental processes and ignoring these processes and the use of a few test images can lead to biased results. By using a qualitative/quantitative (Interpretation Based Quality, IBQ) methodology, we examined the process of pair-wise comparison in a setting, where the quality of the images printed by laser printer on different paper grades was evaluated. Test image consisted of a picture of a table covered with several objects. Three other images were also used, photographs of a woman, cityscape and countryside. In addition to the pair-wise comparisons, observers (N=10) were interviewed about the subjective quality attributes they used in making their quality decisions. An examination of the individual pair-wise comparisons revealed serious inconsistencies in observers' evaluations on the test image content, but not on other contexts. The qualitative analysis showed that this inconsistency was due to the observers' focus of attention. The lack of easily recognizable context in the test image may have contributed to this inconsistency. To obtain reliable knowledge of the effect of image context or attention on subjective image quality, a qualitative methodology is needed.
Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for
specifying the quality potential of printers and materials in more detail than before. Both production and end-use
standpoints are relevant. This paper gives an overview of an
on-going study which has the goal of determining a
framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is
founded on the concept of a layered network model. The model and its subjective, objective and instrumental
measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples
obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color
managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be
accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image
content has a marked effect on the dimensions. This underlines the challenges in designing the test images.
The development of networked, distributed, digital color printing gives rise to new requirements of managing color. In the paper, platform for automatic, source and device independent color correction is defined and its implementation outlined. Moreover, a model for capacity in distributed digital printing is presented.