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Visual artifact
View on Wikipediafrom Wikipedia
Visual artifacts (also artefacts) are anomalies apparent during visual representation as in digital graphics and other forms of imagery, especially photography and microscopy.
In digital graphics
[edit]
- Image quality factors, different types of visual artifacts
- Compression artifacts
- Digital artifacts, visual artifacts resulting from digital image processing
- Noise
- Screen-door effect, also known as fixed-pattern noise (FPN), a visual artifact of digital projection technology
- Ghosting (television)
- Screen burn-in
- Distortion
- Silk screen effect
- Rainbow effect
- Screen tearing
- Moiré pattern
- Color banding
In video entertainment
[edit]Many people who use their computers as a hobby experience artifacting due to a hardware or software malfunction. The cases can differ but the usual causes are:
- Temperature issues, such as failure of cooling fan.
- Unsuited video card (graphics card) drivers.
- Drivers that have values that the graphics card is not suited with.
- Overclocking beyond the capabilities of the particular video card.
- Software bugs in the application or operating system.
The differing cases of visual artifacting can also differ between scheduled task(s).
In photography
[edit]
These effects can occur in both analog and digital photography.
- Chromatic aberration due to optical dispersion through a lens, leading to color fringes at high-contrast boundaries in a photograph
- Motion blur
- Near-camera reflection, visual artifacts caused by the backscatter of light by unfocused particles
In microscopy
[edit],_an_artefact.jpg)
In microscopy, an artifact is an apparent structural detail that is caused by the processing of the specimen and is thus not a legitimate feature of the specimen. In light microscopy, artifacts may be produced by air bubbles trapped under the slide's cover slip.[1]
In electron microscopy, distortions may be produced in the drying out of the specimen. Staining can cause the appearance of solid chemical deposits that may be seen as structures inside the cell. Different techniques including freeze-fracturing and cell fractionation may be used to overcome the problems of artifacts.[1]
A crush artifact is an artificial elongation and distortion seen in histopathology and cytopathology studies, presumably because of iatrogenic compression of tissues. Distortion can be caused by the slightest compression of tissue and can provide difficulties in diagnosis.[2][3] It may cause chromatin to be squeezed out of nuclei.[4] Inflammatory and tumor cells are most susceptible to crush artifacts.[4]
-
Cellulose contamination, in H&E stain and polarized light -
Cardiac muscle (bottom) with contamination from thyroid tissue (center) -
Crush artifact from compression by forceps on the tissue sample -
Folding artifacts (white arrows) and a crush artifact (black arrow, with cytoplasmic hypereosinophilia and nuclear pleomorphism) from a needle -
![Tearing artifacts, such as can be caused by: - Microtomy with a nick or blemish in the knife edge.[5] - Traction of the sections. - Too much or too little alcohol dehydration.[5] - Sectioning calcified parts, which can be decalcified or removed.[5]](//upload.wikimedia.org/wikipedia/commons/thumb/8/80/Microscopy_of_liver_parenchyma_with_tearing_artifacts.jpg/330px-Microscopy_of_liver_parenchyma_with_tearing_artifacts.jpg)
-
Formalin pigment artifacts -
Air bubble entrapment artifact in a shoulder joint biopsy -
Staining artifacts by residual wax, resulting in pale areas where cellular structures are not discernible -
A separation artifact in top image makes the tumor look incompletely excised, but the next microtomy level (bottom image) shows a surgical margin of connective tissue. -
Stacking of cells on top of each other gives a dark look, and in this breast tissue it may mimic microcalcifications. -
Pap stained smear of a monocyte with nuclear smearing or smudging artifact, seen as a tail-like extension of nuclear material -
![Small cell carcinoma is a cancer where the presence of smudging is a clue to the diagnosis.[6]](//upload.wikimedia.org/wikipedia/commons/thumb/9/96/Histopathology_of_small_cell_carcinoma_-_nuclear_smudging.png/250px-Histopathology_of_small_cell_carcinoma_-_nuclear_smudging.png)
Small cell carcinoma is a cancer where the presence of smudging is a clue to the diagnosis.[6]
![Tearing artifacts, such as can be caused by: - Microtomy with a nick or blemish in the knife edge.[5] - Traction of the sections. - Too much or too little alcohol dehydration.[5] - Sectioning calcified parts, which can be decalcified or removed.[5]](https://en.wikipedia.org/wiki/File:Microscopy_of_liver_parenchyma_with_tearing_artifacts.jpg)
.jpg)
![Small cell carcinoma is a cancer where the presence of smudging is a clue to the diagnosis.[6]](https://en.wikipedia.org/wiki/File:Histopathology_of_small_cell_carcinoma_-_nuclear_smudging.png)
In radiography
[edit]In projectional radiography, visual artifacts that can constitute disease mimics include jewelry, clothes and skin folds.[7]
-
A hip fracture (black arrow) next to a skin fold (white arrow) -
Bed sheets looking like lung opacities on a chest radiograph
.jpg)
In magnetic resonance imaging
[edit]In Magnetic resonance imaging, artifacts can be classified as patient-related, signal processing-dependent or hardware (machine)-related.[8]
References
[edit]- ^ a b Kent, Michael (2000). Advanced Biology (Repr. ed.). Oxford: Oxford University Press. p. 64. ISBN 0199141959.
- ^ Chatterjee, S. (September 2014). "Artefacts in histopathology". Journal of Oral and Maxillofacial Pathology. 18 (Suppl 1): S111-6. doi:10.4103/0973-029X.141346. PMC 4211218. PMID 25364159.
- ^ Komanduri S, Swanson G, Keefer L, Jakate S (December 2009). "Use of a new jumbo forceps improves tissue acquisition of Barrett's esophagus surveillance biopsies". Gastrointestinal Endoscopy. 70 (6): 1072–8.e1. doi:10.1016/j.gie.2009.04.009. PMID 19595312.
- ^ a b Chatterjee, Shailja (2014). "Artefacts in histopathology". Journal of Oral and Maxillofacial Pathology. 18 (4): S111 – S116. doi:10.4103/0973-029X.141346. ISSN 0973-029X. PMC 4211218. PMID 25364159.
- ^ a b c Taqi, SyedAhmed; Sami, SyedAbdus; Sami, LateefBegum; Zaki, SyedAhmed (2018). "A review of artifacts in histopathology". Journal of Oral and Maxillofacial Pathology. 22 (2): 279. doi:10.4103/jomfp.JOMFP_125_15. ISSN 0973-029X. PMC 6097380. PMID 30158787.
- ^ Image by Mikael Häggström, MD. Source for findings: Caroline I.M. Underwood, M.D., Carolyn Glass, M.D., Ph.D. "Lung - Small cell carcinoma". Pathology Outlines.
{{cite web}}: CS1 maint: multiple names: authors list (link) Last author update: 20 September 2022 - ^ Page 46 in: Michael Darby, Nicholas Maskell, Anthony Edey, Ladli Chandratreya (2012). Pocket Tutor Chest X-Ray Interpretation. JP Medical Ltd. ISBN 9781907816062.
{{cite book}}: CS1 maint: multiple names: authors list (link) - ^ Erasmus, L.J.; Hurter, D.; Naude, M.; Kritzinger, H.G.; Acho, S. (2004). "A short overview of MRI artifacts". South African Journal of Radiology. 8 (2): 13. doi:10.4102/sajr.v8i2.127. ISSN 2078-6778. (CC-BY 4.0)
Visual artifact
View on Grokipediafrom Grokipedia
A visual artifact is an unintended flaw, distortion, or anomaly that appears in images, videos, or other visual representations, arising from limitations in capture, processing, compression, rendering, or optical systems. These imperfections, such as blockiness or unnatural patterns, degrade the perceptual quality of the media without altering its intended content.[1][2]
Visual artifacts manifest in various forms depending on the digital workflow stage. Common types include compression artifacts, like blockiness and banding from lossy codecs such as JPEG or H.264, which occur when data is discarded to reduce file size; aliasing, or "jaggies," resulting from insufficient sampling of high-frequency details; and noise, such as color speckles introduced by sensor limitations or low-light conditions. Other notable examples are blooming, where bright areas overflow into adjacent pixels, and moiré patterns, interference fringes from overlapping repetitive structures.[3][4][5]
The causes of visual artifacts stem from technical constraints across imaging systems. In acquisition, hardware issues like CMOS sensor crosstalk or X-ray beam hardening in medical imaging produce distortions such as streaking or halos. During processing and rendering, factors including aggressive compression settings, interpolation errors, or shader inefficiencies in graphics lead to anomalies like texture flickering or edge halos. Multiple re-encodings or mismatches in color spaces exacerbate these issues, particularly in video streams.[1][2][6]
Mitigating visual artifacts is crucial for maintaining high-quality digital media in applications ranging from photography to medical diagnostics and gaming. Techniques such as anti-aliasing filters, higher bit-depth encoding, and advanced algorithms like metal artifact reduction in CT scans help minimize distortions, though trade-offs in computational cost or file size often remain. Ongoing research focuses on AI-driven corrections to enhance artifact detection and removal.[7][8]
Compression artifacts in digital graphics files, particularly those using lossy formats like JPEG, introduce blockiness from the discrete cosine transform applied to 8x8 pixel blocks, where quantization discards fine details to reduce file size, creating visible grid-like patterns in uniform areas. In graphics workflows, this affects saved renders or textures, exacerbating issues when blocks align poorly with image content, such as gradients or edges. GPU-specific compression during real-time rendering can compound this with temporal aliasing, where frame-to-frame inconsistencies in motion or lighting cause flickering edges, as low temporal sampling rates fail to capture smooth transitions in dynamic scenes.[32][33]
A notable case study involves early implementations of the CryEngine in games like Far Cry (2004) and Crysis (2007), where limited anti-aliasing and mipmapping optimizations led to prominent edge flickering and texture shimmering, particularly on foliage and distant geometry under motion, highlighting the challenges of real-time rendering on pre-2010s hardware before widespread adoption of advanced temporal filters. These artifacts were partially resolved in later versions like CryEngine 3 through improved supersampling and velocity-based reprojection, demonstrating the evolution of graphics pipelines toward artifact-free output.[34]
General Concepts
Definition and Overview
Visual artifacts refer to unintended distortions, anomalies, or errors that appear in images, videos, or visual displays, deviating from the intended representation of the subject or scene, often arising from technical limitations in capture, processing, or rendering systems. In practical terms, these artifacts introduce unwanted information or omit essential details from the visual output, thereby compromising the fidelity of the depiction.[9] The phenomenon of visual artifacts traces its origins to early 19th-century photography, with processes like the Daguerreotype introduced in 1839 revealing initial challenges in achieving faithful representations due to limitations in light-sensitive materials. These early anomalies highlighted the difficulties of working without modern anti-reflection layers. For instance, halation—caused by light passing through the sensitive emulsion, reflecting off the backing, and creating halos around bright areas—became a notable issue in subsequent emulsion-based processes.[10] In the digital realm, visual artifacts were more formally recognized and studied during the 1960s amid the emergence of computer graphics, as pioneers at institutions like Boeing and MIT developed systems for generating and displaying synthetic images, revealing limitations in resolution and sampling that produced visible errors.[11] Broadly, visual artifacts can be classified into categories such as noise, which involves random pixel variations; distortion, encompassing systematic geometric or colorimetric shifts; aliasing, resulting from inadequate sampling rates that produce false patterns like moiré effects; and compression artifacts, stemming from lossy data reduction techniques that introduce blockiness or blurring. Moiré patterns, for instance, exemplify aliasing as interference fringes arising when repetitive structures in the scene interact with the imaging system's grid.[12][13] These artifacts significantly influence human visual perception by altering how viewers interpret spatial relationships and details, while also undermining data integrity in applications like scientific imaging and compromising user experience in entertainment or interfaces through reduced clarity and realism.[14][15]Common Causes and Prevention
Visual artifacts in imaging systems often arise from fundamental limitations in the digitization process, particularly sampling errors governed by the Nyquist-Shannon sampling theorem. According to this theorem, accurate reconstruction of a continuous signal requires a sampling rate at least twice the highest frequency component; when the sampling rate falls below this threshold, aliasing occurs, manifesting as distortions such as moiré patterns or jagged edges in digital images.[16] Quantization noise represents another primary cause, where continuous analog values are approximated to discrete digital levels, leading to rounding errors that degrade image fidelity and introduce visible banding or contouring in low-contrast areas.[17] Transmission errors during data transfer can further contribute, as random bit flips from noise or interference alter pixel values, resulting in speckle-like artifacts across the image.[18] Environmental factors exacerbate these issues by influencing signal capture and integrity. Inconsistent lighting conditions, such as uneven illumination or varying spectral distributions, can cause exposure variations that amplify noise and create uneven tonal artifacts in captured visuals.[19] Sensor limitations, including finite dynamic range and thermal noise in detectors, limit the ability to faithfully represent scene details, particularly in low-light scenarios where signal-to-noise ratios degrade.[20] Additionally, processing algorithms may introduce systematic bias if they overemphasize certain features, such as aggressive sharpening that generates halo effects around edges, thereby distorting the original visual content.[21] To mitigate these causes, several prevention strategies are employed during image acquisition and processing. Anti-aliasing filters, typically low-pass filters applied before sampling, attenuate high-frequency components to ensure compliance with the Nyquist criterion, thereby preventing aliasing distortions.[22] Dithering techniques add controlled noise to the signal prior to quantization, randomizing errors to reduce perceptible banding and enhance perceived smoothness in gradients.[23] For transmission-related issues, error-correcting codes (ECC) embed redundant data bits to detect and repair bit errors without retransmission, maintaining image integrity in noisy channels.[24] General workflows incorporate pre-processing calibration, such as flat-field correction to normalize sensor responses and adjust for lighting variances, ensuring consistent artifact-free outputs from the outset.[25] Standards and software tools support these preventive measures by providing benchmarks and practical implementations. The ISO 12233 standard outlines methods for assessing resolution and spatial frequency response in electronic still-picture cameras, enabling verification of sampling adequacy and artifact minimization through standardized test charts.[26] In professional workflows, tools like Adobe Photoshop offer artifact reduction features, including neural filters that automatically detect and smooth quantization-induced flaws while preserving detail.[27]Digital Media Artifacts
In Digital Graphics
In digital graphics, rendering artifacts arise from the interactions between software algorithms and hardware in the graphics pipeline, particularly during the conversion of 3D models to 2D images. One common issue is Z-fighting, which occurs when two or more surfaces have nearly identical depth values in the Z-buffer, leading to rapid flickering as the renderer alternates between them due to precision limitations in depth comparisons. This artifact is especially prevalent in scenes with coplanar geometry, such as overlapping polygons in architectural models or terrain rendering, and can be mitigated by adjusting polygon offsets or increasing depth buffer resolution.[28] Texture mapping distortions represent another key class of rendering artifacts, often stemming from improper handling of texture coordinates during projection onto surfaces. When mipmapping fails—such as by selecting an inappropriate resolution level for distant or angled textures—high-frequency details can cause aliasing, manifesting as shimmering or moiré patterns as the viewpoint changes. Mipmapping addresses this by precomputing a pyramid of downsampled texture images, allowing trilinear interpolation to select and blend levels that match the screen-space footprint, thereby reducing these distortions without excessive blurring in close-up views.[29] Spatial aliasing emerges prominently during vector-to-raster conversion in the rasterization stage, where continuous geometric primitives are discretized into pixels, producing jagged edges or "stair-stepping" on diagonal lines and curves due to undersampling high-frequency components. This occurs because the finite pixel grid cannot accurately represent sub-pixel features, leading to misrepresentation of edges in vector-based graphics like fonts or line art. Anti-aliasing techniques, such as supersampling, combat this by rendering at a higher resolution and averaging samples per pixel; for a sample count , the effective reduction in aliasing error follows from variance reduction principles, scaling as , which improves perceived smoothness without fully eliminating high-frequency artifacts.[30][31]The [aliasing](/page/Aliasing) reduction factor in [supersampling](/page/Supersampling) is given by:
$$ \sigma \propto \frac{1}{\sqrt{n}} $$
where $ \sigma $ is the standard deviation of the pixel color variance, and $ n $ is the number of samples per pixel.
The [aliasing](/page/Aliasing) reduction factor in [supersampling](/page/Supersampling) is given by:
$$ \sigma \propto \frac{1}{\sqrt{n}} $$
where $ \sigma $ is the standard deviation of the pixel color variance, and $ n $ is the number of samples per pixel.