Camera System Performance Derived from Natural Scenes

The Modulation Transfer Function (MTF) is a well-established measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Non-linearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds

Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply non- linear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The scene- and-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-process- dependent Modulation Transfer Functions (SPD-MTF)

A tool for deriving camera spatial frequency response from natural scenes (NS-SFR)

Recent research on digital camera performance evaluation introduced the Natural Scene Spatial Frequency Response (NS-SFR) framework, shown to provide a comparable measure to the ISO12233 edge SFR (e-SFR) but derived outside laboratory conditions. The framework extracts step-edges captured from pictorial natural scenes to evaluate the camera SFR. It is in 2-parts. The first utilizes the ISO12233 slanted-edge algorithm to produce an ‘envelope’ of NS-SFRs. The second estimates the system e-SFR from this NS-SFR data. One drawback of this proposed methodology has been the computation time. The process was not optimized, as it first derived NS-SFRs from all suitable step-edges and then further validated and statistically treated the results to estimate the e-SFR. This paper presents changes to the framework processes, aiming to optimize the computation time so that it is practical for real-world implementation. The developments include an improved framework structure, a pixel-stretching filter alternative, and the capability to utilize Graphics Processing Unit (GPU) acceleration. In addition, the methodology was updated to utilize the latest e-SFR algorithm implementation. The resulting code has been incorporated into a self-executable user interface prototype, available in GitHub. Future goals include making it an open-access, cloud-based solution to be used by scientists, camera evaluation labs and the general public

An evaluation of MTF determination methods for 35mm scanners

Three different techniques were used to determine the Modulation Transfer Function (MTF) of a 35mm film scanner. The first involved scanning sine wave charts comprising a number of patches with different frequencies of known modulation. The second method involved the scanning and Fourier transform of a photographic grain noise pattern to simulate low modulation white noise. Finally, the ISO 12233 Slanted-edge Spatial Frequency Response (SFR) plug-in was used to determine the average MTF of the device. This creates a super-sampled edge profile from sequential scan-lines of the sampled image of an edge. Procedures for creating test targets, where appropriate, are described. Advantages and limitations encountered in applying each methodology are discussed, as well as the precision of each method for deriving the MTF. Conclusions are drawn concerning the comparability of MTFs determined by the three methods

Bridging the Gap Between Imaging Performance and Image Quality Measures

Imaging system performance measures and Image Quality Metrics (IQM) are reviewed from a systems engineering perspective, focusing on spatial quality of still image capture systems. We classify IQMs broadly as: Computational IQMs (CPIQM), Multivariate Formalism IQMs (MF-IQM), Image Fidelity Metrics (IF-IQM), and Signal Transfer Visual IQMs (STV-IQM). Comparison of each genre finds STV-IQMs well suited for capture system quality evaluation: they incorporate performance measures relevant to optical systems design, such as Modulation Transfer Function (MTF) and Noise-Power Spectrum (NPS); their bottom, modular approach enables system components to be optimised separately. We suggest that correlation between STV IQMs and observer quality scores is limited by three factors: current MTF and NPS measures do not characterize scene-dependent performance introduced by imaging system non-linearities; contrast sensitivity models employed do not account for contextual masking effects; cognitive factors are not considered. We hypothesise that implementation of scene and process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures should mitigate errors originating from scene dependent system performance. Further, we propose implementation of contextual contrast detection and discrimination models to better represent low-level visual performance in image quality analysis. Finally, we discuss image quality optimization functions that may potentially close the gap between contrast detection/discrimination and quality

Edge Detection Techniques for Quantifying Spatial Imaging System Performance and Image Quality

Measuring camera system performance and associating it directly to image quality is very relevant, whether images are aimed for viewing, or as input to machine learning and automated recognition algorithms. The Modulation Transfer Function (MTF) is a well- established measure for evaluating this performance. This study proposes a novel methodology for measuring system MTFs directly from natural scenes, by adapting the standardized Slanted Edge Method (ISO 12233). The method involves edge detection techniques, to select and extract suitable step edges from pictorial images. The scene MTF aims to account for camera non-linear scene dependent processes. This measure is more relevant to image quality modelling than the traditionally measured MTFs. Preliminary research results indicate that the proposed method can provide reliable MTFs, following the trends of the ISO 12233. Further development and validation are required before it is proposed as a universal camera measuring technique

Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems

The edge Spatial Frequency Response (eSFR) is an established measure for camera system quality performance, traditionally measured under laboratory conditions. With the increasing use of Deep Neural Networks (DNNs) in autonomous vision systems, the input signal quality becomes crucial for optimal operation. This paper proposes a method to estimate the system eSFR (sys-SFR) from pictorial natural scene derived SFRs (NS-SFRs) as previously presented, laying the foundation for adapting the traditional method to a real-time measure. In this study, the NS-SFR input parameter variations are first investigated to establish suitable ranges that give a stable estimate. Using the NS-SFR framework with the established parameter ranges, the system eSFR, as per ISO 12233, is estimated. Initial validation of results is obtained from implementing the measuring framework with images from a linear and a non-linear camera system. For the linear system, results closely approximate the ISO 12233 eSFR measurement. Non-linear system measurements exhibit scene dependant characteristics expected from edge-based methods. The requirements to implement this method in real-time for autonomous systems are then discussed

Scene-and-Process-Dependent Spatial Image Quality Metrics

Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS), and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured using traditional methods. We present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal-transfer and noise scene-dependency, respectively. We also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Further, three leading camera metrics are revised that use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. The novel metrics outperformed existing metrics of the same genre

Protocol for the development of the Master Chemical Mechanism, MCM v3 (Part A): tropospheric degradation of non-aromatic volatile organic compounds

Kinetic and mechanistic data relevant to the tropospheric degradation of volatile organic compounds (VOC), and the production of secondary pollutants, have previously been used to define a protocol which underpinned the construction of a near-explicit Master Chemical Mechanism. In this paper, an update to the previous protocol is presented, which has been used to define degradation schemes for 107 non-aromatic VOC as part of version 3 of the Master Chemical Mechanism (MCM v3). The treatment of 18 aromatic VOC is described in a companion paper. The protocol is divided into a series of subsections describing initiation reactions, the reactions of the radical intermediates and the further degradation of first and subsequent generation products. Emphasis is placed on updating the previous information, and outlining the methodology which is specifically applicable to VOC not considered previously (e.g. <font face='Symbol' >a</font>- and <font face='Symbol' >b</font>-pinene). The present protocol aims to take into consideration work available in the open literature up to the beginning of 2001, and some other studies known by the authors which were under review at the time. Application of MCM v3 in appropriate box models indicates that the representation of isoprene degradation provides a good description of the speciated distribution of oxygenated organic products observed in reported field studies where isoprene was the dominant emitted hydrocarbon, and that the <font face='Symbol' >a</font>-pinene degradation chemistry provides a good description of the time dependence of key gas phase species in <font face='Symbol' >a</font>-pinene/NO_X photo-oxidation experiments carried out in the European Photoreactor (EUPHORE). Photochemical Ozone Creation Potentials (POCP) have been calculated for the 106 non-aromatic non-methane VOC in MCM v3 for idealised conditions appropriate to north-west Europe, using a photochemical trajectory model. The POCP values provide a measure of the relative ozone forming abilities of the VOC. Where applicable, the values are compared with those calculated with previous versions of the MCM

The relationship between exposure to natural and urban environments and children’s self-regulation

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