Image fidelity

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Image fidelity, often referred to as the ability to discriminate between two images [1] or how closely the image represents the real source distribution. [2] Different from image quality, which is often referred to as the subject preference for one image over another, image fidelity represents to the ability of a process to render an image accurately, without any visible distortion or information loss. The two terms are often used interchangeably, but they are not the same. [3]

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If we cannot detect the difference between a photograph and a digitally printed image, we might conclude that the digital print has photographic image quality. [4] But subjective impressions of image quality are much more difficult to characterize and, consequently, nearly impossible to quantify. It is not difficult to demonstrate that people use multiple visual factors or dimensions in complex non-linear combinations to make judgements about image quality. [5] There are also significant individual differences in their judgements. [6]

Image fidelity in photography

In photography, image fidelity is also referred to as micro-contrast or 3D pop. The inner tonal rendition of an image could be found as more shades and details are rendered.

There are three ways to increase image fidelity. [7]

The first is to adopt a high transmission lens on the camera. Lenses with high transmissive characteristics can direct more light into the sensors.

The second is to increase the sensor saturation. There are two ways do this:

The latter is more common. Flash is usually the method used to achieve it.

Third, reducing or removing the color filter array on a camera sensor. To remove or reduce the thickness of the filter array on the camera sensor so that more low-gain light can be recorded. [8] This process is also called "Debayering a sensor". [9]

Related Research Articles

Dynamic range is the ratio between the largest and smallest values that a certain quantity can assume. It is often used in the context of signals, like sound and light. It is measured either as a ratio or as a base-10 (decibel) or base-2 logarithmic value of the difference between the smallest and largest signal values.

<span class="mw-page-title-main">Bayer filter</span> Color filter array

A Bayer filter mosaic is a color filter array (CFA) for arranging RGB color filters on a square grid of photosensors. Its particular arrangement of color filters is used in most single-chip digital image sensors used in digital cameras, camcorders, and scanners to create a color image. The filter pattern is half green, one quarter red and one quarter blue, hence is also called BGGR,RGBG, GRBG, or RGGB.

<span class="mw-page-title-main">Simultaneous localization and mapping</span> Computational navigational technique used by robots and autonomous vehicles

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.

<span class="mw-page-title-main">Sensor fusion</span> Combining of sensor data from disparate sources

Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and WiFi localization signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision.

<span class="mw-page-title-main">Image noise</span> Visible interference in an image

Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that obscures the desired information. Typically the term “image noise” is used to refer to noise in 2D images, not 3D images.

A demosaicing algorithm is a digital image process used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color filter array (CFA). It is also known as CFA interpolation or color reconstruction.

The structural similarityindex measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. SSIM is used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference.

<span class="mw-page-title-main">Image sensor</span> Device that converts images into electronic signals

An image sensor or imager is a sensor that detects and conveys information used to form an image. It does so by converting the variable attenuation of light waves into signals, small bursts of current that convey the information. The waves can be light or other electromagnetic radiation. Image sensors are used in electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, optical mouse devices, medical imaging equipment, night vision equipment such as thermal imaging devices, radar, sonar, and others. As technology changes, electronic and digital imaging tends to replace chemical and analog imaging.

<span class="mw-page-title-main">Color filter array</span>

In digital imaging, a color filter array (CFA), or color filter mosaic (CFM), is a mosaic of tiny color filters placed over the pixel sensors of an image sensor to capture color information.

Warped linear predictive coding is a variant of linear predictive coding in which the spectral representation of the system is modified, for example by replacing the unit delays used in an LPC implementation with first-order all-pass filters. This can have advantages in reducing the bitrate required for a given level of perceived audio quality/intelligibility, especially in wideband audio coding.

A visual sensor network or smart camera network or intelligent camera network is a network of spatially distributed smart camera devices capable of processing, exchanging data and fusing images of a scene from a variety of viewpoints into some form more useful than the individual images. A visual sensor network may be a type of wireless sensor network, and much of the theory and application of the latter applies to the former. The network generally consists of the cameras themselves, which have some local image processing, communication and storage capabilities, and possibly one or more central computers, where image data from multiple cameras is further processed and fused. Visual sensor networks also provide some high-level services to the user so that the large amount of data can be distilled into information of interest using specific queries.

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". 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.

A flame detector is a sensor designed to detect and respond to the presence of a flame or fire, allowing flame detection. Responses to a detected flame depend on the installation, but can include sounding an alarm, deactivating a fuel line, and activating a fire suppression system. When used in applications such as industrial furnaces, their role is to provide confirmation that the furnace is working properly; it can be used to turn off the ignition system though in many cases they take no direct action beyond notifying the operator or control system. A flame detector can often respond faster and more accurately than a smoke or heat detector due to the mechanisms it uses to detect the flame.

<span class="mw-page-title-main">Optical braille recognition</span> Automated recognition of braille characters

Optical braille recognition is technology to capture and process images of braille characters into natural language characters. It is used to convert braille documents for people who cannot read them into text, and for preservation and reproduction of the documents.

Diagnostically acceptable irreversible compression (DAIC) is the amount of lossy compression which can be used on a medical image to produce a result that does not prevent the reader from using the image to make a medical diagnosis.

Dynamic texture is the texture with motion which can be found in videos of sea-waves, fire, smoke, wavy trees, etc. Dynamic texture has a spatially repetitive pattern with time-varying visual pattern. Modeling and analyzing dynamic texture is a topic of images processing and pattern recognition in computer vision.

An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise.

Leonid Rudin is an American computer scientist known as the co-founder and CEO of Cognitech. He is one of the leaders in the Forensic Video Image processing field.

<span class="mw-page-title-main">Video super-resolution</span> Generating high-resolution video frames from given low-resolution ones

Video super-resolution (VSR) is the process of generating high-resolution video frames from the given low-resolution video frames. Unlike single-image super-resolution (SISR), the main goal is not only to restore more fine details while saving coarse ones, but also to preserve motion consistency.

The Papoulis-Marks-Cheung approach is a theorem in multidimensional Shannon sampling theory that shows that the sampling density of a two-dimensional bandlimited function can be reduced to the support of the Fourier transform of the function. Applying a multidimensional generalization of a theorem by Athanasios Papoulis, the approach was first proposed by Robert J. Marks II and Kwang Fai Cheung. The approach has been called "elegant," "remarkably" closed, and "interesting."

References

  1. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. Vol. 1. pp. 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN   0-7803-3259-8. S2CID   17711937.
  2. Wright, M. (2000). "2000ASPC..217..344W Page 344". Imaging at Radio Through Submillimeter Wavelengths. 217: 344. Bibcode:2000ASPC..217..344W . Retrieved 2020-12-03.
  3. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. Vol. 1. pp. 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN   0-7803-3259-8. S2CID   17711937.
  4. Silverstein, D. A.; Farrell, J. E. (September 1996). "The relationship between image fidelity and image quality". Proceedings of 3rd IEEE International Conference on Image Processing. Vol. 1. pp. 881–884 vol.1. doi:10.1109/ICIP.1996.559640. ISBN   0-7803-3259-8. S2CID   17711937.
  5. "IMAGE QUALITY: A MULTIDIMENSIONAL PROBLEM" (PDF). NASA.
  6. Multidimensional Scaling: Theory and Applications in the behavioral sciences. New York: Seminar Press. 1972. pp. 105–156. ISBN   978-0127857817.
  7. "The FACTS of IMAGE FIDELITY & its existence". Youtube.
  8. Design, Wild Dog (2017-09-22). ""Monochroming" a colour sensor and colour photography with the Monochrom - Wild Dog Design" . Retrieved 2020-12-03.
  9. Design, Wild Dog (2017-09-22). ""Monochroming" a colour sensor and colour photography with the Monochrom - Wild Dog Design" . Retrieved 2020-12-03.

Further reading