Image quality can refer to the level of accuracy with which different imaging systems capture, process, store, compress, transmit and display the signals that form an image. Another definition refers to image quality as "the weighted combination of all of the visually significant attributes of an image".[1]: 598 The difference between the two definitions is that one focuses on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers.
Image quality should not be mistaken with image fidelity. Image fidelity refers to the ability of a process to render a given copy in a perceptually similar way to the original (without distortion or information loss), i.e., through a digitization or conversion process from analog media to digital image.
The process of determining the level of accuracy is called Image Quality Assessment (IQA). Image quality assessment is part of the quality of experience measures. Image quality can be assessed using two methods: subjective and objective. Subjective methods are based on the perceptual assessment of a human viewer about the attributes of an image or set of images, while objective methods are based on computational models that can predict perceptual image quality.[2]: vii Objective and subjective methods aren't necessarily consistent or accurate between each other: a human viewer might perceive stark differences in quality in a set of images where a computer algorithm might not.
Subjective methods are costly, require a large number of people, and are impossible to automate in real-time. Therefore, the goal of image quality assessment research is to design algorithms for objective assessment that are also consistent with subjective assessments.[3] The development of such algorithms has a lot of potential applications. They can be used to monitor image quality in control quality systems, to benchmark image processing systems and algorithms and to optimize imaging systems.[2]: 2 [3]: 430
The image formation process is affected by several distortions between the moment in which the signals travel through to and reach the capture surface, and the device or mean in which signals are displayed. Although optical aberrations can cause great distortions in image quality, they are not part of the field of Image Quality Assessment. Optical aberrations caused by lenses belong to the optics area and not to the signal processing areas.
In an ideal model, there's no quality loss between the emission of the signal and the surface in which the signal is being captured on. For example, a digital image is formed by electromagnetic radiation or other waves as they pass through or reflect off objects. That information is then captured and converted into digital signals by an image sensor. The sensor, however, has nonidealities that limit its performance.
Image quality assessment methods
Image quality can be assessed using objective or subjective methods. In the objective method, image quality assessments are performed by different algorithms that analyze the distortions and degradations introduced in an image. Subjective image quality assessments are a method based on the way in which humans experience or perceive image quality. Objective and subjective methods of quality assessment don't necessarily correlate with each other. An algorithm might have a similar value for an image and its altered or degraded versions, while a subjective method might perceive a stark contrast in quality for the same image and its versions.
Subjective methods for image quality assessment belong to the larger area of psychophysics research, a field that studies the relationship between physical stimulus and human perceptions. A subjective IQA method will typically consist on applying mean opinion score techniques, where a number of viewers rate their opinions based on their perceptions of image quality. These opinions are afterwards mapped onto numerical values.
These methods can be classified depending on the availability of the source and test images:
Single-stimulus: the viewer only has the test image and is not aware of the source image.
Double-stimulus: the viewer has both the source and test image.
Since visual perception can be affected by environmental and viewing conditions, the International Telecommunication Union produced a set of recommendations for standardized testing methods for subjective image quality assessment.[4]
Objective methods
Wang & Bovik (2006) classify the objective methods with the following criteria: (a) the availability of an original image; (b) on the basis of their application scopes and (c) on the model of a Human Visual System simulation to assess quality.[5] Keelan (2002) classifies the methods based on (a) direct experimental measurements; (b) system modeling and (c) visual assessment against calibrated standards.[6]: 173
Full-reference (FR) methods – FR metrics try to assess the quality of a test image by comparing it with a reference image that is assumed to have perfect quality, e.g. the original of an image versus a JPEG-compressed version of the image.
Reduced-reference (RR) methods – RR metrics assess the quality of a test and reference image based on a comparison of features extracted from both images.
No-reference (NR) methods – NR metrics try to assess the quality of a test image without any reference to the original one.
Image quality metrics can also be classified in terms of measuring only one specific type of degradation (e.g., blurring, blocking, or ringing), or taking into account all possible signal distortions, that is, multiple kinds of artifacts.[7]
Image quality attributes
Blown highlights are detrimental to image quality. Top: Original image. Bottom: Blown areas highlighted in red.
At full resolution, this image has clearly visible compression artifacts, for example along the edges of the rightmost trusses.
Sharpness determines the amount of detail an image can convey. System sharpness is affected by the lens (design and manufacturing quality, focal length, aperture, and distance from the image center) and sensor (pixel count and anti-aliasing filter). In the field, sharpness is affected by camera shake (a good tripod can be helpful), focus accuracy, and atmospheric disturbances (thermal effects and aerosols). Lost sharpness can be restored by sharpening, but sharpening has limits. Oversharpening, can degrade image quality by causing "halos" to appear near contrast boundaries. Images from many compact digital cameras are sometimes oversharpened to compensate for lower image quality.
Noise is a random variation of image density, visible as grain in film and pixel level variations in digital images. It arises from the effects of basic physics— the photon nature of light and the thermal energy of heat— inside image sensors. Typical noise reduction (NR) software reduces the visibility of noise by smoothing the image, excluding areas near contrast boundaries. This technique works well, but it can obscure fine, low contrast detail.
Dynamic range (or exposure range) is the range of light levels a camera can capture, usually measured in f-stops, EV (exposure value), or zones (all factors of two in exposure). It is closely related to noise: high noise implies low dynamic range.
Contrast, also known as gamma, is the slope of the tone reproduction curve in a log-log space. High contrast usually involves loss of dynamic range — loss of detail, or clipping, in highlights or shadows.
Color accuracy is an important but ambiguous image quality factor. Many viewers prefer enhanced color saturation; the most accurate color isn't necessarily the most pleasing. Nevertheless, it is important to measure a camera's color response: its color shifts, saturation, and the effectiveness of its white balance algorithms.
Distortion is an aberration that causes straight lines to curve. It can be troublesome for architectural photography and metrology (photographic applications involving measurement). Distortion tends to be noticeable in low cost cameras, including cell phones, and low cost DSLR lenses. It is usually very easy to see in wide angle photos. It can be now be corrected in software.
Vignetting, or light falloff, darkens images near the corners. It can be significant with wide angle lenses.
Exposure accuracy can be an issue with fully automatic cameras and with video cameras where there is little or no opportunity for post-exposure tonal adjustment. Some even have exposure memory: exposure may change after very bright or dark objects appear in a scene.
Lateral chromatic aberration (LCA), also called "color fringing", including purple fringing, is a lens aberration that causes colors to focus at different distances from the image center. It is most visible near corners of images. LCA is worst with asymmetrical lenses, including ultrawides, true telephotos and zooms. It is strongly affected by demosaicing.
Lens flare, including "veiling glare" is stray light in lenses and optical systems caused by reflections between lens elements and the inside barrel of the lens. It can cause image fogging (loss of shadow detail and color) as well as "ghost" images that can occur in the presence of bright light sources in or near the field of view.
Color moiré is artificial color banding that can appear in images with repetitive patterns of high spatial frequencies, like fabrics or picket fences. It is affected by lens sharpness, the anti-aliasing (lowpass) filter (which softens the image), and demosaicing software. It tends to be worst with the sharpest lenses.
Artifacts – software (especially operations performed during RAW conversion) can cause significant visual artifacts, including data compression and transmission losses (e.g. Low quality JPEG), oversharpening "halos" and loss of fine, low-contrast detail.
^Burningham, Norman; Pizlo, Zygmunt; Allebach, Jan P. (2002). "Image Quality Metrics". In Hornak, Joseph P. (ed.). Encyclopedia of imaging science and technology. New York: Wiley. doi:10.1002/0471443395.img038. ISBN978-0-471-33276-3.
^ abWang, Zhou; Bovik, Alan C. (2006). "Preface". Modern image quality assessment. San Rafael: Morgan & Claypool Publishers. ISBN978-1598290226.
^ abSheikh, Hamid Rahim; Bovik, Alan C. (February 2006). "Image Information and Visual Quality". IEEE Transactions on Image Processing. 15 (2): 430–444. Bibcode:2006ITIP...15..430S. CiteSeerX10.1.1.477.2659. doi:10.1109/TIP.2005.859378. PMID 16479813.
^P.910 : Subjective video quality assessment methods for multimedia applications. International Telecommunication Union. 6 April 2008. [dead link]
^Zhou Wang; Alan C. Bovik (2006). Modern Image Quality Assessment. pp. 11–15. ISBN1-59829-022-3. OL 9866061M. Wikidata Q55757889.
^Keelan, Brian W. (2002). Handbook of image quality : characterization and prediction. New York, NY: Marcel Dekker, Inc. ISBN978-0-8247-0770-5.
^Shahid, Muhammad; Rossholm, Andreas; Lövström, Benny; Zepernick, Hans-Jürgen (2014-08-14). "No-reference image and video quality assessment: a classification and review of recent approaches". EURASIP Journal on Image and Video Processing. 2014: 40. doi:10.1186/1687-5281-2014-40. ISSN 1687-5281.
Further reading
Sheikh, H.R.; Bovik A.C., Information Theoretic Approaches to Image Quality Assessment. In: Bovik, A.C. Handbook of Image and Video Processing. Elsevier, 2005.
Guangyi Chen, Stephane Coulombe, An Image Visual Quality Assessment Method Based on SIFT Features 85-97 JPRR
image, quality, refer, level, accuracy, with, which, different, imaging, systems, capture, process, store, compress, transmit, display, signals, that, form, image, another, definition, refers, image, quality, weighted, combination, visually, significant, attri. Image quality can refer to the level of accuracy with which different imaging systems capture process store compress transmit and display the signals that form an image Another definition refers to image quality as the weighted combination of all of the visually significant attributes of an image 1 598 The difference between the two definitions is that one focuses on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers Image quality should not be mistaken with image fidelity Image fidelity refers to the ability of a process to render a given copy in a perceptually similar way to the original without distortion or information loss i e through a digitization or conversion process from analog media to digital image The process of determining the level of accuracy is called Image Quality Assessment IQA Image quality assessment is part of the quality of experience measures Image quality can be assessed using two methods subjective and objective Subjective methods are based on the perceptual assessment of a human viewer about the attributes of an image or set of images while objective methods are based on computational models that can predict perceptual image quality 2 vii Objective and subjective methods aren t necessarily consistent or accurate between each other a human viewer might perceive stark differences in quality in a set of images where a computer algorithm might not Subjective methods are costly require a large number of people and are impossible to automate in real time Therefore the goal of image quality assessment research is to design algorithms for objective assessment that are also consistent with subjective assessments 3 The development of such algorithms has a lot of potential applications They can be used to monitor image quality in control quality systems to benchmark image processing systems and algorithms and to optimize imaging systems 2 2 3 430 Contents 1 Image quality factors 2 Image quality assessment methods 3 Subjective methods 4 Objective methods 5 Image quality attributes 6 See also 7 References 8 Further readingImage quality factors EditThe image formation process is affected by several distortions between the moment in which the signals travel through to and reach the capture surface and the device or mean in which signals are displayed Although optical aberrations can cause great distortions in image quality they are not part of the field of Image Quality Assessment Optical aberrations caused by lenses belong to the optics area and not to the signal processing areas In an ideal model there s no quality loss between the emission of the signal and the surface in which the signal is being captured on For example a digital image is formed by electromagnetic radiation or other waves as they pass through or reflect off objects That information is then captured and converted into digital signals by an image sensor The sensor however has nonidealities that limit its performance Image quality assessment methods EditImage quality can be assessed using objective or subjective methods In the objective method image quality assessments are performed by different algorithms that analyze the distortions and degradations introduced in an image Subjective image quality assessments are a method based on the way in which humans experience or perceive image quality Objective and subjective methods of quality assessment don t necessarily correlate with each other An algorithm might have a similar value for an image and its altered or degraded versions while a subjective method might perceive a stark contrast in quality for the same image and its versions Subjective methods EditMain article Subjective video quality Subjective methods for image quality assessment belong to the larger area of psychophysics research a field that studies the relationship between physical stimulus and human perceptions A subjective IQA method will typically consist on applying mean opinion score techniques where a number of viewers rate their opinions based on their perceptions of image quality These opinions are afterwards mapped onto numerical values These methods can be classified depending on the availability of the source and test images Single stimulus the viewer only has the test image and is not aware of the source image Double stimulus the viewer has both the source and test image Since visual perception can be affected by environmental and viewing conditions the International Telecommunication Union produced a set of recommendations for standardized testing methods for subjective image quality assessment 4 Objective methods EditWang amp Bovik 2006 classify the objective methods with the following criteria a the availability of an original image b on the basis of their application scopes and c on the model of a Human Visual System simulation to assess quality 5 Keelan 2002 classifies the methods based on a direct experimental measurements b system modeling and c visual assessment against calibrated standards 6 173 Full reference FR methods FR metrics try to assess the quality of a test image by comparing it with a reference image that is assumed to have perfect quality e g the original of an image versus a JPEG compressed version of the image Reduced reference RR methods RR metrics assess the quality of a test and reference image based on a comparison of features extracted from both images No reference NR methods NR metrics try to assess the quality of a test image without any reference to the original one Image quality metrics can also be classified in terms of measuring only one specific type of degradation e g blurring blocking or ringing or taking into account all possible signal distortions that is multiple kinds of artifacts 7 Image quality attributes Edit Blown highlights are detrimental to image quality Top Original image Bottom Blown areas highlighted in red At full resolution this image has clearly visible compression artifacts for example along the edges of the rightmost trusses Sharpness determines the amount of detail an image can convey System sharpness is affected by the lens design and manufacturing quality focal length aperture and distance from the image center and sensor pixel count and anti aliasing filter In the field sharpness is affected by camera shake a good tripod can be helpful focus accuracy and atmospheric disturbances thermal effects and aerosols Lost sharpness can be restored by sharpening but sharpening has limits Oversharpening can degrade image quality by causing halos to appear near contrast boundaries Images from many compact digital cameras are sometimes oversharpened to compensate for lower image quality Noise is a random variation of image density visible as grain in film and pixel level variations in digital images It arises from the effects of basic physics the photon nature of light and the thermal energy of heat inside image sensors Typical noise reduction NR software reduces the visibility of noise by smoothing the image excluding areas near contrast boundaries This technique works well but it can obscure fine low contrast detail Dynamic range or exposure range is the range of light levels a camera can capture usually measured in f stops EV exposure value or zones all factors of two in exposure It is closely related to noise high noise implies low dynamic range Tone reproduction is the relationship between scene luminance and the reproduced image brightness Contrast also known as gamma is the slope of the tone reproduction curve in a log log space High contrast usually involves loss of dynamic range loss of detail or clipping in highlights or shadows Color accuracy is an important but ambiguous image quality factor Many viewers prefer enhanced color saturation the most accurate color isn t necessarily the most pleasing Nevertheless it is important to measure a camera s color response its color shifts saturation and the effectiveness of its white balance algorithms Distortion is an aberration that causes straight lines to curve It can be troublesome for architectural photography and metrology photographic applications involving measurement Distortion tends to be noticeable in low cost cameras including cell phones and low cost DSLR lenses It is usually very easy to see in wide angle photos It can be now be corrected in software Vignetting or light falloff darkens images near the corners It can be significant with wide angle lenses Exposure accuracy can be an issue with fully automatic cameras and with video cameras where there is little or no opportunity for post exposure tonal adjustment Some even have exposure memory exposure may change after very bright or dark objects appear in a scene Lateral chromatic aberration LCA also called color fringing including purple fringing is a lens aberration that causes colors to focus at different distances from the image center It is most visible near corners of images LCA is worst with asymmetrical lenses including ultrawides true telephotos and zooms It is strongly affected by demosaicing Lens flare including veiling glare is stray light in lenses and optical systems caused by reflections between lens elements and the inside barrel of the lens It can cause image fogging loss of shadow detail and color as well as ghost images that can occur in the presence of bright light sources in or near the field of view Color moire is artificial color banding that can appear in images with repetitive patterns of high spatial frequencies like fabrics or picket fences It is affected by lens sharpness the anti aliasing lowpass filter which softens the image and demosaicing software It tends to be worst with the sharpest lenses Artifacts software especially operations performed during RAW conversion can cause significant visual artifacts including data compression and transmission losses e g Low quality JPEG oversharpening halos and loss of fine low contrast detail See also EditMean squared error MSE Peak signal to noise ratio PSNR Structural similarity SSIM Visual Information Fidelity VIF Human visual system Subjective video quality Video quality Just noticeable difference PsychophysicsReferences Edit Burningham Norman Pizlo Zygmunt Allebach Jan P 2002 Image Quality Metrics In Hornak Joseph P ed Encyclopedia of imaging science and technology New York Wiley doi 10 1002 0471443395 img038 ISBN 978 0 471 33276 3 a b Wang Zhou Bovik Alan C 2006 Preface Modern image quality assessment San Rafael Morgan amp Claypool Publishers ISBN 978 1598290226 a b Sheikh Hamid Rahim Bovik Alan C February 2006 Image Information and Visual Quality IEEE Transactions on Image Processing 15 2 430 444 Bibcode 2006ITIP 15 430S CiteSeerX 10 1 1 477 2659 doi 10 1109 TIP 2005 859378 PMID 16479813 P 910 Subjective video quality assessment methods for multimedia applications International Telecommunication Union 6 April 2008 dead link Zhou Wang Alan C Bovik 2006 Modern Image Quality Assessment pp 11 15 ISBN 1 59829 022 3 OL 9866061M Wikidata Q55757889 Keelan Brian W 2002 Handbook of image quality characterization and prediction New York NY Marcel Dekker Inc ISBN 978 0 8247 0770 5 Shahid Muhammad Rossholm Andreas Lovstrom Benny Zepernick Hans Jurgen 2014 08 14 No reference image and video quality assessment a classification and review of recent approaches EURASIP Journal on Image and Video Processing 2014 40 doi 10 1186 1687 5281 2014 40 ISSN 1687 5281 Further reading EditSheikh H R Bovik A C Information Theoretic Approaches to Image Quality Assessment In Bovik A C Handbook of Image and Video Processing Elsevier 2005 Guangyi Chen Stephane Coulombe An Image Visual Quality Assessment Method Based on SIFT Features 85 97 JPRR Hossein Ziaei Nafchi Atena Shahkolaei Rachid Hedjam Mohamed Cheriet Mean Deviation Similarity Index Efficient and Reliable Full Reference Image Quality Evaluator In IEEE Access IEEE Retrieved from https en wikipedia org w index php title Image quality amp oldid 1113231083, wikipedia, wiki, book, books, library,
