In this paper, we present a new video database: CVD2014-Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process. CVD2014 contains a total of 234 videos that are recorded using 78 different cameras. Moreover, this database contains the observer-specific quality evaluation scores rather than only providing mean opinion scores. We have also collected open-ended quality descriptions that are provided by the observers. These descriptions were used to define the quality dimensions for the videos in CVD2014. The dimensions included sharpness, graininess, color balance, darkness, and jerkiness. At the end of this paper, a performance study of image and video quality algorithms for predicting the subjective video quality is reported. For this performance study, we proposed a new performance measure that accounts for observer variance. The performance study revealed that there is room for improvement regarding the video quality assessment algorithms. The CVD2014 video database has been made publicly available for the research community. All video sequences and corresponding subjective ratings can be obtained from the CVD2014 project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

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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 pipeline's 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.

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 naïve 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.

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 1 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.