General Image Quality Equation Research Articles

Evaluation of jitter vibration-induced image interpretability degradation for remote sensing data using GIQE

Remote sensing data plays an important role in various military and civilian applications. The quality of remote sensing data is the key to the success of these applications. remote sensing data are subjected to a variety of distortions from different sources, including acquisition, onboard compression, transmission, on-ground processing, and satellite vibrations. These distortions cause a great loss of image quality. In this paper, jitter vibration is modeled according to the modulation transfer function (MTF). Jitter-induced degradation is evaluated in terms of the National Imagery Interpretability Rating Scale (NIIRS), which is measured in terms of the general image quality equation (GIQE). A study on the third, fourth, and fifth versions of the GIQE is made using a remote sensing data set to select the appropriate version that could be used in evaluating the amount of jitter-induced degradation. The key result of this paper is that the fourth version of the GIQE is the most proper one in evaluating the amount of jitter-induced degradation.

A Remote Sensing Image Quality Interpretation Scale Characterization Method Based on the TTP Criterion

Accurate grading of remote sensing image interpretation is crucial for improving image classification and screening efficiency. Through extensive research, the General Image Quality Equation (GIQE) based on the National Imagery Interpretability Rating Scale (NIIRS) has been developed. However, poor reliability and low accuracy issues remain due to the failure to consider human visual characteristics. This paper introduces the Target Task Performance (TTP) criterion as a key parameter to reflect the cascading degradation factors of human visual perception characteristics and system imaging links, which improves the reliability of the model. A New Optimized Remote Sensing Image Quality Equation (NORSIQE), which effectively predicted the interpretability of image information, is constructed. Using 200 sets of test data, the quantitative relationship between key parameters (GSD, TTP, and SNR) of the NORSIQE and the subjective NIIRS level is obtained by a least squares regression fit, and the determination coefficient of the model is as high as 0.916. The model is evaluated for accuracy using 120 sets of validation data, showing an 87% improvement compared to the GIQE4. This method provides theoretical support for the development of new methods for remote sensing image quality evaluation and the design of payloads for remote sensing imaging systems.

Image quality and computer vision performance: assessing the effects of image distortions and modeling performance relationships using the general image quality equation

Substantial research has explored methods to optimize convolutional neural networks (CNNs) for tasks such as image classification and object detection, but research into the image quality drivers of computer vision performance has been limited. Additionally, there are indications that image degradations such as blur and noise affect human visual interpretation and machine interpretation differently. The general image quality equation (GIQE) predicts overhead image quality for human analysis using the National Image Interpretability Rating Scale, but no such model exists to predict image quality for interpretation by CNNs. Here, we assess the relationship between image quality variables and CNN performance. Specifically, we examine the impacts of resolution, blur, and noise on CNN performance for models trained with in-distribution and out-of-distribution distortions. Using two datasets, we observe that while generalization remains a significant challenge for CNNs faced with out-of-distribution image distortions, CNN performance against low visual quality images remains strong with appropriate training, indicating the potential to expand the design trade space for sensors providing data to computer vision systems. Additionally, we find that CNN performance predictions using the functional form of the GIQE can predict CNN performance as a function of image degradation, but we observe that the legacy form of the GIQE (from GIQE versions 3 and 4) does a better job of modeling the impact of blur/relative edge response in our experiments.

RADIOMETRIC QUALITY ASSESSMENT FOR MAXAR HD IMAGERY

Abstract. The requirement for very high-resolution satellite imagery by different applications has been increasing continuously. Several commercial and government-supported missions provide sub-meter spatial resolutions from optical sensors aboard Earth Observation (EO) satellites. The MAXAR satellite constellation acquires images with up to 30 cm Ground Sampling Distances (GSDs); and the High-Definition (HD) image production technology developed by MAXAR doubles the resolution by using artificial intelligence methods. Although the spatial resolution is one of the most important image quality metrics, several other factors indicated by diverse radiometric and geometric characteristics may circumscribe the usability of data in different projects. As part of mandatory activities of European Space Agency (ESA), Earthnet Programme provides a framework for integrating Third-Party Missions into the overall EO strategy and promotes the international use of the data. The Earthnet Data Assessment Pilot (EDAP) project aims at assessing the quality and the suitability of TPMs, and provides a communication platform between mission providers to ensure the coherence of the systems. In this study, the radiometric quality of the MAXAR HD products was evaluated within the EDAP project framework by using several General Image-Quality Equation (GIQE) metrics, visual inspections, and comparative assessments with orthophotos obtained from an Unmanned Aerial Vehicle (UAV) platform and with the original (non-HD) orthophotos with 30 cm resolutions. The results show that the spatial resolution improvements are observable in urban areas, where sharp edges are present. However, blurring and color noise patterns also occured in the HD images.

Airborne validation of the general image quality equation 5.

The general image quality equation (GIQE) maps imaging system parameters to performance in terms of common detection and recognition tasks. Changes to the National Imagery Interpretation Rating Scale led to the development of GIQE version 5 in 2015. The purpose of this paper is to provide a review of GIQE 5 applications in the literature, a tutorial on its use, and, for the first time , an independent validation of the new model along with comparisons to GIQEs 3 and 4.

COMPARATIVE STUDY OF THE DIFFERENT VERSIONS OF THE GENERAL IMAGE QUALITY EQUATION

Abstract. The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE 3, GIQE 4 and GIQE 5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE 4, denominated GIQE 4C, that provides better correlation with GIQE 3 and GIQE 5. The second objective of this paper is to compare the predictions of GIQE 4C and GIQE 5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE 4 to GIQE 5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.

EO Sensor Planning for UAV Engineering Reconnaissance Based on NIIRS and GIQE

When unmanned aerial vehicles (UAVs) support the Corps of Engineers in reconnaissance operations, in order to gather visible image information that should meet the mission’s need, we grouped the engineering reconnaissance information interpretation tasks into 10 levels by using the National Imagery Interpretability Rating Scale (NIIRS). The quantitative relationship between the engineering targets, sensor performance, and flight altitude was established through the general image quality equation (GIQE) and the geometrical property of the ground sampled distance (GSD). Through some simulations, the influence of variable factors of the EO sensor imaging quality was analyzed, and the imaging height of the sensor for an engineering reconnaissance scenario was calculated. The results showed that this study could solve the problem of poor image quality caused by the flight altitude not meeting the mission requirements.

Image quality modeling and characterization of Nyquist-sampled framing sensors with operational considerations for remote sensing

The effect of increasing Q on image interpretability is explored, and the fidelity of general image quality equation (GIQE) predictions is assessed for Nyquist-sampled (Q=2) imagery at low signal-to-noise ratio. A digital image chain simulation is developed and validated against a laboratory test bed using objective and subjective assessments. Using the validated model, additional test cases are simulated to study the effects of increased detector sampling on image quality with operational considerations for space-based remote sensing. Variants of the GIQE are evaluated against subject-provided ratings, and modifications that increase prediction accuracy for Q=2 imagery are proposed. Finally, using the validated simulation and modified image quality equation, trades are conducted to ascertain the feasibility of implementing Q=2 designs in future electro-optical systems.

Reducing the risk of laser damage in a focal plane array using linear pupil-plane phase elements.

A compact imaging system with reduced risk of damage owing to intense laser radiation is presented. We find that a pupil phase element may reduce the peak image plane irradiance from an undesirable laser source by two orders of magnitude, thereby protecting the detector from damage. The desired scene is reconstructed in postprocessing. The general image quality equation (GIQE) [Appl. Opt.36, 8322 (1997)] is used to estimate the interpretability of the resulting images. A localized loss of information caused by laser light is also described. This system may be advantageous over other radiation protection approaches because accurate pointing and nonlinear materials are not required.

Emitter selection criteria for passive multistatic synthetic aperture radar imaging

Passive radar provides a means of spectrum sharing in a congested environment. In passive radar, the radar design problem is one of emitter selection, rather than waveform optimisation. This study combines relevant criteria (signal-to-noise ratio, ambiguity function integrated sidelobes, effective multistatic resolution area and contrast ratio) in a multiobjective optimisation to select subsets of available emitters for passive multistatic synthetic aperture radar (SAR). Extensions to well-known monostatic and bistatic performance criteria are defined for the multistatic SAR case. Assumed limitations in the number of available receiver channels constrain the emitter selection. The proposed emitter selection framework is demonstrated for a simulated scenario of 24 analogue and digital emitters, with a constraint of four receiver channels. Results indicate that the proposed ranking successfully differentiates between emitter sets resulting in good images and those sets resulting in poor images, providing a first step towards formulating a general image quality equation for multistatic SAR.

Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data

Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 (Ziyuan-3) satellite is the first civil high resolution mapping satellite in China, which was established in 2012. So far, there has been no reports on adopting NIIRS as the common indicator for the quality assessment of that satellite image data. This lack of a common quality indicator results in a gap between satellite data users around the world and those in China regarding the understanding of the quality and usability of ZY-3 data. To overcome the gap, using the general image-quality equation (GIQE), this study evaluates the ZY-3 sensor-corrected (SC) panchromatic nadir (NAD) data in terms of the NIIRS. In order to solve the uncertainty resulting from the exceeding of the ground sample distance (GSD) of ZY-3 data (2.1 m) in GIQE (less than 2.03 m), eight images are used to establish the relationship between the manually obtained NIIRS and the GIQE predicted NIIRS. An adjusted GIQE is based on the relationship and verified by another five images. Our study demonstrates that the method of using adjusted GIQE for calculating NIIRS can be used for the quality assessment of ZY-3 satellite images and reveals that the NIIRS value of ZY-3 SC NAD data is about 2.79.

Application of the general image-quality equation to aberrated imagery

The regression analysis for the general image-quality equation (GIQE) [Appl. Opt. 36, 8322-8328 (1997)] was performed using image data from well-corrected optical systems. We conducted human-subject experiments to examine the use of the GIQE with aberrated imagery. A modified image-quality equation for aberrated imagery is presented based on analysis of the experimental results.

Synthetic aperture radar target acquisition model based on a National Imagery Interpretability Rating Scale to probability of discrimination conversion

Previous efforts have established models that relate IR and visible imaging sensor design and operating parameter values to measures of information extraction performance. The surveillance and reconnaissance community predicts National Imagery Interpretability Rating Scale (NIIRS) values using the general image quality equation (GIQE). The target acquisition community predicts probabilities of discrimination (detection, recognition, identification) using various minimum resolvable contrast/minimum resolvable temperature (MRC/MRT) models, and methods have been developed to link the two sets of predictions. No such models currently exist for the radar domain, although a radar NIIRS has been developed. We provide a basis for initial estimates of synthetic aperture radar (SAR) target acquisition performance. First, a conversion from radar to IR (and visible) NIIRS is provided. The IR NIIRS is then converted to probabilities of discrimination values. The two conversions together provide rough estimates of SAR target acquisition performance as a function of radar NIIRS values.

General image-quality equation for infrared imagery

The regression-based general image-quality equation (GIQE) previously developed for visible imagery was extended to IR imagery. The equation predicts IR National Imagery Interpretability Rating Scale values as a function of scale, sharpness, and signal-to-noise ratio with the same form as the visible GIQE.

Sensor performance conversions for infrared target acquisition and intelligence–surveillance–reconnaissance imaging sensors

Target acquisition infrared imaging sensors are characterized by their minimum resolvable temperature parameter that is translated to the probability of identification (Pid) performance estimate for a given target. Intelligence-surveillance-reconnaissance (ISR) sensors are characterized by the general image quality equation to give a national imagery interpretability rating scale (NIIRS) performance estimate. Sensors, such as those on Predator and Global Hawk, will soon be used for both ISR and target acquisition purposes. We present a performance conversion that includes both sensor resolution and sensitivity. We also provide the first empirical results to our knowledge ever to be presented that relate NIIRS and Pid for a given set of targets.

Targeting and intelligence electro-optical recognition modeling: a juxtaposition of the probabilities of discrimination and the general image quality equation

The recognition of objects using target acquisition systems is modeled by a sensor's minimum resolvable temperature (MRT), the Johnson criteria, atmospherics, and object specifics. Collectively, these three characteristics provide an acquisition model for estimating the probability of object recognition (and detection, identification) as a func- tion of sensor-to-object range. This technique is called the probabilities of discrimination. When quantifying the performance of intelligence- surveillance-reconnaissance (ISR) systems, object recognition is as- sessed using the National Imagery Interpretability Scale (NIIRS). Each NIIRS level corresponds to a different capacity for object recognition and is defined by a set of recognition criteria. The general image quality equation (GIQE) is the ISR sensor model that determines the expected NIIRS level of a sensor for a given set of sensor parameters. It is impor- tant that electro-optical sensor engineers understand both of these rec- ognition models. The segregation between the target acquisition and ISR sensor communities is becoming less sharp as ISR sensors are begin- ning to be used for target acquisition purposes and visa versa. Network and wireless communication advances are providing the means for dual exploitation of these systems. Descriptions of these two recognition models, probabilities of discrimination, and the GIQE are provided. The two models are applied to example systems. Finally, the two models are compared and contrasted. © 1998 Society of Photo-Optical Instrumentation Engi- neers. (S0091-3286(98)00503-0)

General Image-Quality Equation: GIQE

A regression-based model was developed relating aerial image quality, expressed in terms of the National Imagery Interpretability Rating Scale (NIIRS), to fundamental image attributes. The General Image-Quality Equation (GIQE) treats three main attributes: scale, expressed as the ground-sampled distance; sharpness, measured from the system modulation transfer function; and the signal-to-noise ratio. The GIQE can be applied to any visible sensor and predicts NIIRS ratings with a standard error of 0.3 NIIRS. The image attributes treated by the GIQE are influenced by system design and operation parameters. The GIQE allows system designers and operators to perform trade-offs for the optimization of image quality.