The National Telecommunications and Information Administration (NTIA) General Model for estimating video quality and its associated calibration techniques were independently evaluated by the Video Quality Experts Group (VQEG) in their Phase II Full Reference Television (FR-TV) test. The NTIA General Model was the only video quality estimator that was in the top performing group for both the 525-line and 625-line video tests. As a result, the American National Standards Institute (ANSI) adopted the NTIA General Model and its associated calibration techniques as a North American Standard in 2003. The International Telecommunication Union (ITU) has also included the NTIA General Model as a normative method in two Draft Recommendations. This paper presents a description of the NTIA General Model and its associated calibration techniques. The independent test results from the VQEG FR-TV Phase II tests are summarized, as well as results from eleven other subjective data sets that were used to develop the method.

A new standardized method for objectively measuring video quality

Present day no-reference/no-reference image quality assessment (NR IQA) algorithms usually assume that the distortion affecting the image is known. This is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. We propose a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS). Once trained, the framework does not require any knowledge of the distorting process and the framework is modular in that it can be extended to any number of distortions. We describe the framework for blind image quality assessment and a version of this framework-the blind image quality index (BIQI) is evaluated on the LIVE image quality assessment database. A software release of BIQI has been made available online: http://live.ece.utexas.edu/research/quality/BIQI_release.zip.

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A Two-Step Framework for Constructing Blind Image Quality Indices

Perceptual visual quality metrics: A survey

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.

A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

An Underwater Color Image Quality Evaluation Metric

Sharpness metrics that use the whole frequency spectrum of the image cannot separate the sharpness information from the scene content. The sharpness metrics that use spatial gradients of the edges work only for comparisons among versions of the same image. We have developed a content independent, no-reference sharpness metric based on the local frequency spectrum around the image edges. In this approach, we create an edge profile by detecting edge pixels and assigning them to 8×8 pixel blocks. Then we compute sharpness using the average 2D kurtosis of the 8×8 DCT blocks. However, average kurtosis is highly sensitive to asymmetry in the DCT, e.g. different amounts of energy and edges in the x and y directions, therefore causing problems with different content and asymmetric sharpness enhancement. Thus we compensate the kurtosis using spatial edge extent information and the amount of vertical and horizontal energy in the DCT. The results show high correlation with subjective quality for sharpness-enhanced video and high potential to deal with asymmetric enhancement. For compressed, extremely sharpened and noisy video, the metric correlates with subjective scores up to the point where impairments become strongly noticeable in the subjective quality evaluation. The metric can be used by itself as a control variable for high-quality image capture and display systems, high-quality sharpness enhancement algorithms, and as a key component of a more general overall quality metric.

A new sharpness metric based on local kurtosis, edge and energy information

In this paper we present a no-reference objective quality metric (NROQM) that has resulted from extensive research on impairment metrics, image feature metrics, and subjective image quality in several projects in Philips Research, and participation in the ITU Video Quality Experts Group. The NROQM is aimed at requirements including video algorithm development, embedded monitoring and control of image quality, and evaluation of different types of display systems. NROQM is built from metrics for desirable and non-desirable image features (sharpness, contrast, noise, clipping, ringing, and blocking artifacts), and accounts for their individual and combined contributions to perceived image quality. We describe our heuristic, incremental approach to modeling quality and training the NROQM, and its advantages to deal with imperfect data and imperfect metrics. The results of training the NROQM using a large set of video sequences, which include degraded and enhanced video, show high correlation between objective and subjective scores, and the results of the first performance test show good objective-subjective correlations as well. We also discuss issues that require further research such as fully content-independent metrics, measuring over-enhanced video quality, and the role of temporal impairment metrics.

No-Reference Quality Metric for Degraded and Enhanced Video

This paper presents a subjective experiment performed to determine the optimal level of sharpness enhancement for various image content and two display technologies. For the experiment, a peaking algorithm was used as sharpness enhancement method and its gain parameter, which controls the amount of sharpness enhancement, was optimized. Eleven still images were used as image material, and for each original, eight levels of sharpness enhancement were shown. Subjects were asked to select the image that they prefer most on overall image quality. To study the effect of the display technology, the experiment was performed on a CRT monitor and LCD panel. The results show an effect of content: lower gains were preferred for faces and content with flat areas, while higher gains were preferred for highly textured images. We also found an effect of display: in general for a given image, the averaged gain preferred on the CRT was equal or higher than on the LCD. The results can be used to optimize sharpness enhancement algorithms to image content and display type in agreement with the averaged preference of viewers.

Franco Oberti

Papers

A new sharpness metric based on local kurtosis, edge and energy information

No-Reference Quality Metric for Degraded and Enhanced Video

Evaluation of optimal sharpness enhancement for different image content and different display technologies