Mikko Nuutinen

Method for measuring stereo camera depth accuracy based on stereoscopic vision

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 cameras depth accuracy than baseline. The tests showed that normal stereo acuity was achieved only using a tele lens. However, a users 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.

A new method for evaluating the subjective image quality of photographs: dynamic reference

The Dynamic Reference (DR) method has been developed for subjective image quality experiments in which original or undistorted images are unavailable. The DR method creates reference image series from test images. Reference images are presented to observers as a slide show prior to evaluating their quality. As the observers view the set of reference images, they determine the overall variation in quality within the set of test images. This study compared the performance of the DR method to that of the standardized absolute category rating (ACR) and paired comparison (PC) methods. We measured the performance of each method in terms of time effort and discriminability. The results showed that the DR method is faster than the PC method and more accurate than the ACR method. The DR method is especially suitable for experiments that require highly accurate results in a short time.

Image feature subsets for predicting the quality of consumer camera images and identifying quality dimensions

Abstract. 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.

A framework for measuring sharpness in natural images captured by digital cameras based on reference image and local areas

Image quality is a vital criterion that guides the technical development of digital cameras. Traditionally, the image quality of digital cameras has been measured using test-targets and/or subjective tests. Subjective tests should be performed using natural images. It is difficult to establish the relationship between the results of artificial test targets and subjective data, however, because of the different test image types. We propose a framework for objective image quality metrics applied to natural images captured by digital cameras. The framework uses reference images captured by a high-quality reference camera to find image areas with appropriate structural energy for the quality attribute. In this study, the framework was set to measure sharpness. Based on the results, the mean performance for predicting subjective sharpness was clearly higher than that of the state-of-the-art algorithm and test-target sharpness metrics.

VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations

This article presents VQone, a graphical experiment builder, written as a MATLAB toolbox, developed for image and video quality ratings. VQone contains the main elements needed for the subjective image and video quality rating process. This includes building and conducting experiments and data analysis. All functions can be controlled through graphical user interfaces. The experiment builder includes many standardized image and video quality rating methods. Moreover, it enables the creation of new methods or modified versions from standard methods. VQone is distributed free of charge under the terms of the GNU general public license and allows code modifications to be made so that the program’s functions can be adjusted according to a user’s requirements. VQone is available for download from the project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

Spatial and Temporal Information as Camera Parameters for Super-resolution Video

Most modern consumer cameras are capable of video capture, but their spatial resolution is generally lower than that of still images. The spatial resolution of videos can be enhanced with a hybrid camera system that combines information from high-resolution still images with low-resolution video frames in a process known as super-resolution. As this process is computationally intensive, we propose a camera system that uses the spatial and temporal information measures SI and TI standardized by ITU as camera parameters to determine during capture whether super-resolution processing would result in an increase in perceived quality. Experimental results show that the difference of these two measures can be used to determine the feasibility of super-resolution processing.

Reference image method for measuring quality of photographs produced by digital cameras

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 benchmarking.

Method for evaluating tone mapping operators for natural high dynamic range images

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 research purposes.

Performance measure of image and video quality assessment algorithms: subjective root-mean-square error

Abstract. Evaluating algorithms used to assess image and video quality requires performance measures. Traditional performance measures (e.g., Pearson’s linear correlation coefficient, Spearman’s rank-order correlation coefficient, and root mean square error) compare quality predictions of algorithms to subjective mean opinion scores (mean opinion score/differential mean opinion score). We propose a subjective root-mean-square error (SRMSE) performance measure for evaluating the accuracy of algorithms used to assess image and video quality. The SRMSE performance measure takes into account dispersion between observers. The other important property of the SRMSE performance measure is its measurement scale, which is calibrated to units of the number of average observers. The results of the SRMSE performance measure indicate the extent to which the algorithm can replace the subjective experiment (as the number of observers). Furthermore, we have presented the concept of target values, which define the performance level of the ideal algorithm. We have calculated the target values for all sample sets of the CID2013, CVD2014, and LIVE multiply distorted image quality databases.The target values and MATLAB implementation of the SRMSE performance measure are available on the project page of this study.

Features for Predicting Quality of Images Captured by Digital Cameras

Algorithmic image quality metrics have been based on the assumption that an image is only distorted by a single distortion type at a time. The performance of the current metrics is low if image concurrently includes more than one distortion. The aim of this study was to find efficient feature sets for predicting visual quality of real photographs which are subjected to many different distortion sources and types. Features should support each other and function with many concurrent image distortions. We used correlation based feature selector method and image database created with various digital cameras for feature selection. Based on the study the results are promising. Our general and scene-specific feature combinations correlate well with the human observations compared to the state-of-the-art metrics.