Jake Elwes () is a British media artist, hacker and researcher. Their practice is the exploration of artificial intelligence (AI), queer theory and technical biases. They are known for using AI to create art in mediums such as video, performance and installation. Elwes considers themselves to be neuroqueer, and their work on queering technology addresses issues caused by the normative biases of artificial intelligence. == Education and early life == Elwes was born in London to British contemporary artist and painter Luke Elwes and Anneke, daughter of Hans Dumoulin. Elwes is the great grandchild of Army officer James Hennessy and portrait painter Simon Elwes RA, son of Victorian opera singer Gervase Elwes. Elwes studied at the Slade School of Fine Art from 2013 to 2017, where they began using computer code as a medium. In 2016 they attended the School of Machines, Making & Make-Believe in Berlin with artist and educator Gene Kogan. Elwes was introduced to drag performance by their collaborator Dr Joe Parslow who holds a PhD in drag performance. Drag performance has since become instrumental to Elwes' work. == Career == Elwes' work with artificial intelligence is cited as a hopeful strategy to make AI more playful and diverse. Elwes' work has been exhibited in numerous international art museums and galleries and was featured in a BBC documentary on the history of video art, they were a 2021 finalist for the Lumen Prize, and received the Honorary Mention of the 2022 Prix Ars Electronica in the Interactive Art + category. They also curated and presented the opening provocation "The New Real - Artistic and Queer Visions of AI Futures" to the UK government with two drag artists at the AI UK conference 2024. Elwes is part of the Radical Faeries countercultural movement. They have exhibited in museums and galleries across Europe and Asia including: Victoria and Albert Museum (London, UK) - The Zizi Show (2023-2024) for the first digital commission in their photography center's digital gallery Pinakothek der Moderne (Munich, Germany) - Glitch. Die Kunst Der Störung (2023-2024) ZKM (Karlsruhe, Germany) - Biomedia (2021-2022) National Museum of Modern and Contemporary Art (Cheongju, South Korea) - What an Artificial World (2024) Somerset House (London, UK) - The Horror Show! (2022-2023) Gazelli Art House (London, UK) - Jake Elwes: Data • Glitch • Utopia (2023) (survey exhibition) Jut Art Museum (Taipei, Taiwan) - Future Lives, Future You (2023-2024) Max Ernst Museum (Brühl, Germany) - Surreal Futures (2023-2024) Zabludowicz Collection (London, UK) - Among the Machines (2022) Ars Electronica (Linz, Austria) - Prix Ars Electronica, CyberArts Exhibition (2022) Institute of Contemporary Arts (ICA) (London, UK) - Do Androids Dream on Silver Screens? (2023) Arebyte gallery (London, UK) - Real-Time Constraints (2020) Ming Contemporary Art Museum (McaM) (Shanghai, China) - Mind the Deep (2019) HMKV (Hartware MedienKunstVerein) (Dortmund, Germany) - House of Mirrors: Artificial Intelligence as Phantasm (2022) Today Art Museum (Beijing, China) - Future of Today: DEJA VU (2019) Science Gallery (Dublin, Ireland) - BIAS (2021-2022) Yuz Museum (Shanghai, China) - Lying Sophia and Mocking Alexa (2021) Fotomuseum Winterthur The Onassis Foundation (Athens, Greece) - You and AI (2021) Royal College of Art (London, UK) - Event Two (2019) (50th anniversary of Computer Arts Society & Event One) Museum für Naturkunde (Berlin, Germany) - Forschungsfall Nachtigall (2019) Frankfurter Kunstverein (Frankfurt, Germany) - I am here to learn (2018) Nature Morte (Delhi, India) - Gradient Descent (2018) BALTIC Centre for Contemporary Art (Newcastle, UK) - Bloomberg New Contemporaries (2017) == Artworks == === The Zizi Project - a deepfake drag cabaret === The Zizi Project is a series of works that explore the interaction of drag and A.I. Currently, The Zizi Project is made up of multiple artworks. ==== Zizi - Queering the Dataset (2019) ==== Knowing that facial recognition technology statically struggle to recognize black women or transgender people, Elwes set out to "Queer the Dataset" through an open-sourced generative adversarial network (GAN, a type of machine learning model and an early Generative artificial intelligence). Elwes added a dataset of 1,000 photos of drag kings and queens into the GAN's 70,000 faces collected in a standardised facial recognition dataset called Flickr-Faces-HQ Dataset (FFHQ). They then created new simulacra faces, known as deep fakes. "We queer that data so it shifts all of the weights in this neural network from a space of normativity into a space of queerness and otherness. Suddenly all of the faces start to break down and you see mascara dissolve into lipstick and blue eye shadow turn into a pink wig" said Elwes in a 2023 interview for Artnet. ==== Zizi & Me (2020–2023) ==== Zizi & Me is an ongoing multimedia collaboration between drag queen Me The Drag Queen and a deepfake A.I. clone of Me The Drag Queen. Using neural networks trained on filmed footage, the project creates a virtual body that can mimic reference movements. The first act, which features a digital lip-sync duet to Anything You Can Do (I Can Do Better), satirises the idea of A.I. being mistaken for a human, using drag performance and cabaret to critique societal narratives about A.I. and its role in shaping identity. The project is part of The Zizi Project by Jake Elwes, which explores the intersection of drag performance and A.I. ==== The Zizi Show - A Deepfake Drag Cabaret (2020) ==== The Zizi Show is a deep fake drag act based on artificial intelligence (AI). It has been presented live and as interactive online artwork. It is an exploration of queer culture and the algorithms philosophy and ethics of AI. The Zizi Show was exhibited as the inaugural exhibition in the digital gallery at the V&A’s Photography Center from 2023 to 2024. ==== Zizi in Motion: A Deepfake Drag Utopia (Movement by Wet Mess) (2023) ==== "Zizi in Motion" is a multichannel silent video installation featuring AI-generated deepfake performances, which are dynamically re-animated through the movements of London drag artist Wet Mess. The movements of Wet Mess cause the AI-generated visuals to glitch and distort, showcasing the interaction between drag performance and artificial intelligence. The work explore the potential for queer communities to ethically and creatively reclaim and repurpose deepfake technology, using it to celebrate queer bodies and identities. === Art in the Cage of Digital Reproduction (2024) === In an act of protest on 26 November 2024, Elwes facilitated indirect access to an early access token for OpenAI’s Sora text-to-video model through a Hugging Face frontend under the account "PR Puppets". The accompanying statement called to 'denormalize the exploitation of artists by major AI companies for training data, R&D, and publicity'. The incident attracted international press coverage calling into question the role of artists in shaping the future of generative AI versus merely serving as data and credibility providers for tech giants. Elwes also coordinated a collection of mini essays with responses and reflections from the signees and guest writers titled "Art in the Cage of Digital Reproduction". === Installations exploring interpretation and feedback loops between neural networks === Elwes has created works based on the interpretations and misinterpretations between different neural networks and training datasets including: A.I. Interprets A.I. Interpreting ‘Against Interpretation’ (Sontag 1966) from 2023, Closed Loop from 2017, and Auto-Encoded Buddha from 2016. ==== A.I. Interprets A.I. Interpreting ‘Against Interpretation’ (Sontag 1966) (2023) ==== A.I. Interprets A.I. Interpreting ‘Against Interpretation (Sontag 1966) is a three-channel video artwork where an AI interprets Susan Sontag’s essay into images, and then and another AI reinterprets those images back into language. The piece highlights how AI-generated art can misinterpret and introduce bias. ==== Closed Loop (2017) ==== Closed Loop is a two-channel video where two neural networks engage in a continuous feedback loop, one generating images based on the text output and the other creating text based on the image output. The work explores how AI models misinterpret and evolve in a surreal, self-perpetuating conversation, without human input. ==== Auto-Encoded Buddha (2016) ==== Auto-Encoded Buddha is a mixed-media piece where an AI attempts to generate an image of a Buddha statue, trained on 5,000 Buddha images. The AI struggles to accurately represent the Buddha, highlighting the limitations of early generative neural networks. The work is a tribute to Nam June Paik’s TV Buddha (1974). === CUSP (2019) === In their video work CUSP (2019) Elwes places marsh birds generated using artificial intelligence into a tidal landscape. These digitally generated and constantly shifting birds are recorded in dialogue with native
Deep image prior
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics are captured by the structure of a convolutional image generator rather than by any previously learned capabilities. == Method == === Background === Inverse problems such as noise reduction, super-resolution, and inpainting can be formulated as the optimization task x ∗ = m i n x E ( x ; x 0 ) + R ( x ) {\displaystyle x^{}=min_{x}E(x;x_{0})+R(x)} , where x {\displaystyle x} is an image, x 0 {\displaystyle x_{0}} a corrupted representation of that image, E ( x ; x 0 ) {\displaystyle E(x;x_{0})} is a task-dependent data term, and R(x) is the regularizer. Deep neural networks learn a generator/decoder x = f θ ( z ) {\displaystyle x=f_{\theta }(z)} which maps a random code vector z {\displaystyle z} to an image x {\displaystyle x} . The image corruption method used to generate x 0 {\displaystyle x_{0}} is selected for the specific application. === Specifics === In this approach, the R ( x ) {\displaystyle R(x)} prior is replaced with the implicit prior captured by the neural network (where R ( x ) = 0 {\displaystyle R(x)=0} for images that can be produced by a deep neural networks and R ( x ) = + ∞ {\displaystyle R(x)=+\infty } otherwise). This yields the equation for the minimizer θ ∗ = a r g m i n θ E ( f θ ( z ) ; x 0 ) {\displaystyle \theta ^{}=argmin_{\theta }E(f_{\theta }(z);x_{0})} and the result of the optimization process x ∗ = f θ ∗ ( z ) {\displaystyle x^{}=f_{\theta ^{}}(z)} . The minimizer θ ∗ {\displaystyle \theta ^{}} (typically a gradient descent) starts from a randomly initialized parameters and descends into a local best result to yield the x ∗ {\displaystyle x^{}} restoration function. ==== Overfitting ==== A parameter θ may be used to recover any image, including its noise. However, the network is reluctant to pick up noise because it contains high impedance while useful signal offers low impedance. This results in the θ parameter approaching a good-looking local optimum so long as the number of iterations in the optimization process remains low enough not to overfit data. === Deep Neural Network Model === Typically, the deep neural network model for deep image prior uses a U-Net like model without the skip connections that connect the encoder blocks with the decoder blocks. The authors in their paper mention that "Our findings here (and in other similar comparisons) seem to suggest that having deeper architecture is beneficial, and that having skip-connections that work so well for recognition tasks (such as semantic segmentation) is highly detrimental." == Applications == === Denoising === The principle of denoising is to recover an image x {\displaystyle x} from a noisy observation x 0 {\displaystyle x_{0}} , where x 0 = x + ϵ {\displaystyle x_{0}=x+\epsilon } . The distribution ϵ {\displaystyle \epsilon } is sometimes known (e.g.: profiling sensor and photon noise) and may optionally be incorporated into the model, though this process works well in blind denoising. The quadratic energy function E ( x , x 0 ) = | | x − x 0 | | 2 {\displaystyle E(x,x_{0})=||x-x_{0}||^{2}} is used as the data term, plugging it into the equation for θ ∗ {\displaystyle \theta ^{}} yields the optimization problem m i n θ | | f θ ( z ) − x 0 | | 2 {\displaystyle min_{\theta }||f_{\theta }(z)-x_{0}||^{2}} . === Super-resolution === Super-resolution is used to generate a higher resolution version of image x. The data term is set to E ( x ; x 0 ) = | | d ( x ) − x 0 | | 2 {\displaystyle E(x;x_{0})=||d(x)-x_{0}||^{2}} where d(·) is a downsampling operator such as Lanczos that decimates the image by a factor t. === Inpainting === Inpainting is used to reconstruct a missing area in an image x 0 {\displaystyle x_{0}} . These missing pixels are defined as the binary mask m ∈ { 0 , 1 } H × V {\displaystyle m\in \{0,1\}^{H\times V}} . The data term is defined as E ( x ; x 0 ) = | | ( x − x 0 ) ⊙ m | | 2 {\displaystyle E(x;x_{0})=||(x-x_{0})\odot m||^{2}} (where ⊙ {\displaystyle \odot } is the Hadamard product). The intuition behind this is that the loss is computed only on the known pixels in the image, and the network is going to learn enough about the image to fill in unknown parts of the image even though the computed loss doesn't include those pixels. This strategy is used to remove image watermarks by treating the watermark as missing pixels in the image. === Flash–no-flash reconstruction === This approach may be extended to multiple images. A straightforward example mentioned by the author is the reconstruction of an image to obtain natural light and clarity from a flash–no-flash pair. Video reconstruction is possible but it requires optimizations to take into account the spatial differences. == Implementations == A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow A Keras-based implementation written in Python 2 and released under the GPLv3: machine_learning_denoising == Example == See Astronomy Picture of the Day (APOD) of 2024-02-18
Uniphore
Uniphore is an American software company that develops artificial intelligence platforms for business use. The company is headquartered in Palo Alto, California, with offices in the United States, United Kingdom, Spain, Israel, United Arab Emirates, and India. Uniphore is known for its "Business AI Cloud," an enterprise AI platform that combines data, knowledge, models, and software agents for use in sales, marketing, and service. The company has also acquired firms in video emotion AI, AI agents, low-code automation, knowledge automation, voice and screen capture, customer data platforms, and data engineering. == History == Uniphore Software Systems was founded by Umesh Sachdev and Ravi Saraogi in 2008 and was incubated at IIT Madras. The company received an initial grant of $100,000 from the National Research Development Corporation. Early work focused on speech technologies for emerging markets. Uniphore partnered with companies that specialized in English and European languages, and adapting the technology for Indian languages and dialects. In 2014, Uniphore released its first flagship products, auMina, along with two other products, Akeira and amVoice. Uniphore raised series A funding, led by Kris Gopalakrishnan (cofounder of Infosys), in April 2015. The next month, Uniphore received additional investment from IDG Ventures. With input from its investors, Uniphore changed its business model from license fee-based income to a software as a service-based subscription fee model in 2015. By June 2016, it had added more than 70 global languages and expanded its services to Southeast Asia, the Middle East, and the United States. The company opened operations in Singapore in October 2016. The company raised Series B funding in October 2017, led by John Chambers and existing investors. Series C funding of $51 million was announced in August 2019 and led by March Capital. Uniphore acquired an exclusive third-party license for robotic process automation technology from NTT DATA in October 2020. In January 2021, Uniphore acquired Emotion Research Lab, a startup based in Spain that uses artificial intelligence and machine learning to analyze video and interpret emotions. The company received $140 million in Series D funding, led by Sorenson Capital Partners, in March 2021, bringing total funding to $210 million. In January 2021, Uniphore acquired Emotion Research Lab. In July 2021, it agreed to acquire Jacada, a provider of low-code/no-code automation; the transaction closed in October 2021. On February 16, 2022, Uniphore announced a $400 million Series E financing led by NEA, which valued the company at $2.5 billion. Hilarie Koplow-McAdams, an NEA venture partner and former Salesforce/New Relic executive, joined Uniphore's board in 2022. Uniphore's board has also included former Cisco CEO John Chambers, former Convergys CEO Andrea J. Ayers, and CrowdStrike CFO Burt Podbere (appointed January 2021). In February 2023, Uniphore acquired UK-based Red Box, a platform for capturing voice and screen recordings used in regulated and large-scale environments. It also acquired France-based Hexagone, a behavioral analytics firm combining computer vision and natural-language techniques. On December 5, 2024, Uniphore announced agreements to acquire ActionIQ, a customer data platform (CDP) vendor, and Infoworks, an enterprise data engineering platform. Uniphore launched the Business AI Cloud on June 9, 2025. The Business AI Cloud consists of a single, unified platform that includes data, knowledge, AI models, and AI agents. Uniphore announced in August 2025 that it had acquired Orby AI and intended to acquire Autonom8 to extend multi-agent and workflow automation capabilities. As of September 2025, Uniphore's customers included the United States Coast Guard, Singapore Police Force, London Underground, DirecTV, JPMorgan Chase, LG, DHL, UPS, Vodafone, Verizon, NTT Data, and as of May 2021, Firstsource. In October 2025, Uniphore raised $260 million in a Series F round at a reported valuation of $2.5 billion. Investors included March Capital, NEA, Nvidia, AMD, Snowflake, and Databricks. In January 2026, KPMG and Uniphore announced a collaboration focused on deploying AI agents powered by specialized small language models. The announcement was made at the World Economic Forum held in Davos. Cognizant and Uniphore announced a partnership in February 2026 to develop industry-specific AI tools for regulated sectors, which would initially focus on life sciences and finance. Uniphore and Rackspace also announced a partnership in March 2026. This partnership was announced in order to create an "Infrastructure-to-Agents" architecture, focusing on Business AI as a private cloud service. == Products == As of 2025, Uniphore's core offering is the Business AI Cloud and Business AI Suite of agentic AI applications. === Business AI Cloud === Uniphore’s Business AI Cloud is a full-stack platform that organizes enterprise data and knowledge for agentic AI applications. The platform enables deployment across clouds and existing data sources. Key layers and capabilities include the following. Agentic layer: Includes prebuilt agents, a natural-language agent builder, and orchestration based on Business Process Model and Notation (BPMN) to run AI workflows across business units. Model layer: Supports an open, interoperable mix of closed and open-source large language models (LLMs). Models can be orchestrated, governed, and replaced as needed. Knowledge layer: Organizes raw data into structured knowledge used for retrieval, explainability, and fine-tuning of small language models (SLMs). Data layer: Connects to data across multiple platforms and clouds through a zero-copy, composable fabric, enabling in-place preparation and supporting data residency and sovereignty requirements. === Business AI Suite === The Uniphore Business AI Suite has various prebuilt AI agents that can be used in customer service, sales, marketing, and human resources. The Uniphore Business AI Suite includes several LOBs (Lines of Business) for business functions with intelligent agents that are prebuilt, but composable. Built on the Uniphore Business AI Cloud, each application combines agentic automation and fine-tuned models. Marketing AI, Customer Service AI, Sales AI, and People AI (for human resources) are included. Competitors include Palantir, Microsoft Azure, Amazon Bedrock, Google's Vertex AI, Databricks, and Snowflake. == Recognition == Deloitte Technology Fast 50 India identified Uniphore as the 17th fastest-growing technology company in India in 2012 and one of the top 500 fastest growing companies in the Asia-Pacific region in 2014. In 2016, Time included Sachdev on its list of "10 millennials who are changing the world" for “building a phone that can understand almost any language”. NASSCOM named Uniphore to its "League of 10" emerging Indian technology companies in 2017. In 2020, the San Francisco Business Times ranked Uniphore as No. 7 among small companies in its list of the best places to work in the San Francisco Bay Area. In 2022, the company was featured on the Forbes AI 50 list. Uniphore was mentioned in the Deloitte Technology Fast 500 list in 2023, 2024, and 2025. In 2025, Inc. included Uniphore in its Best in Business program.
Prism Video Converter
Prism is a multi-format video converter developed by NCH Software for Windows and Mac OS. It offers converting tools for instant media conversions. Prism Video Converter can handle large and high-quality resolution media files. It provides built-in compressor and adjuster settings, allowing users to customize and optimize their videos according to their needs. The software also includes features such as previewing videos and adding effects. Prism offers a free version for non-commercial use as well as a premium version. == Features == Prism Video File Converter supports a wide range of file formats. It enables users to convert videos into formats like AVI, ASF, WMV, MP4, 3GP, etc. It offers the ability to convert DVDs into various formats. It provides tools for adjusting colour and filter options. Prism Video File Converter provides several customizable options for tweaking the output files during the conversion process. Users can adjust compression/encoder rates, set the resolution and frame rate, and specify the desired output file size. The software also offers various effects like video rotation, captions, watermarks, and text overlay. It also includes a built-in preview feature, that enables users to view their videos before and after the conversion process. It supports batch conversion and running conversion in background. == Controversy == Previously, Prism and certain other NCH Software products were bundled with optional browser plugins, including the Google Chrome toolbar and the Conduit toolbar. This resulted in user complaints and raised concerns from antivirus software companies like Norton and McAfee, which flagged them as potential malware. NCH Software has since removed all toolbars, browsers, and third-party app offerings in all Prism versions.
Screenpal
ScreenPal (formerly known as Screencast-O-Matic) is cross-platform screen capture and screen recording software originally developed in 2006. == History == The company was founded by AJ Gregory in 2006 as Screencast-O-Matic. The software includes features for screen recording, screenshot capture, video editing, image editing, and a video and image hosting service. It is available for Windows and Mac operating systems, and has mobile apps for iOS and Android. The company launched a video editor in 2015. It began offering free video and image hosting in 2019, with premium hosting options for subscribers. In 2023, it was rebranded as ScreenPal.
ASR-complete
ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term is typically used informally. Such problems are hypothesised to include: Spoken natural language understanding Understanding speech from far-field microphones, i.e. handling the reverbation and background noise These problems are easy for humans to do (in fact, they are described directly in terms of imitating humans). Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.
Ordered dithering
Ordered dithering is any image dithering algorithm which uses a pre-set threshold map tiled across an image. It is commonly used to display a continuous image on a display of smaller color depth. For example, Microsoft Windows uses it in 16-color graphics modes. With the most common "Bayer" threshold map, the algorithm is characterized by noticeable crosshatch patterns in the result. == Threshold map == The algorithm reduces the number of colors by applying a threshold map M to the pixels displayed, causing some pixels to change color, depending on the distance of the original color from the available color entries in the reduced palette. The first threshold maps were designed by hand to minimise the perceptual difference between a grayscale image and its two-bit quantisation for up to a 4x4 matrix. An optimal threshold matrix is one that for any possible quantisation of color has the minimum possible texture so that the greatest impression of the underlying feature comes from the image being quantised. It can be proven that for matrices whose side length is a power of two there is an optimal threshold matrix. The map may be rotated or mirrored without affecting the effectiveness of the algorithm. This threshold map (for sides with length as power of two) is also known as a Bayer matrix or, when unscaled, an index matrix. For threshold maps whose dimensions are a power of two, the map can be generated recursively via: M 2 n = 1 ( 2 n ) 2 [ 4 M n 4 M n + 2 J n 4 M n + 3 J n 4 M n + J n ] = J 2 ⊗ M n + 1 n 2 M 2 ⊗ J n , {\displaystyle \mathbf {M} _{2n}={\frac {1}{(2n)^{2}}}{\begin{bmatrix}4\mathbf {M} _{n}&4\mathbf {M} _{n}+2\mathbf {J} _{n}\\4\mathbf {M} _{n}+3\mathbf {J} _{n}&4\mathbf {M} _{n}+\mathbf {J} _{n}\end{bmatrix}}=\mathbf {J} _{2}\otimes \mathbf {M} _{n}+{\frac {1}{n^{2}}}\mathbf {M} _{2}\otimes \mathbf {J} _{n},} where J n {\displaystyle \mathbf {J} _{n}} are n × n {\displaystyle n\times n} matrices of ones and ⊗ {\displaystyle \otimes } is the Kronecker product. While the metric for texture that Bayer proposed could be used to find optimal matrices for sizes that are not a power of two, such matrices are uncommon as no simple formula for finding them exists, and relatively small matrix sizes frequently give excellent practical results (especially when combined with other modifications to the dithering algorithm). This function can also be expressed using only bit arithmetic: M(i, j) = bit_reverse(bit_interleave(bitwise_xor(i, j), i)) / n ^ 2 == Pre-calculated threshold maps == Rather than storing the threshold map as a matrix of n {\displaystyle n} × n {\displaystyle n} integers from 0 to n 2 {\displaystyle n^{2}} , depending on the exact hardware used to perform the dithering, it may be beneficial to pre-calculate the thresholds of the map into a floating point format, rather than the traditional integer matrix format shown above. For this, the following formula can be used: Mpre(i,j) = Mint(i,j) / n^2 This generates a standard threshold matrix. for the 2×2 map: this creates the pre-calculated map: Additionally, normalizing the values to average out their sum to 0 (as done in the dithering algorithm shown below) can be done during pre-processing as well by subtracting 1⁄2 of the largest value from every value: Mpre(i,j) = Mint(i,j) / n^2 – 0.5 maxValue creating the pre-calculated map: == Algorithm == The ordered dithering algorithm renders the image normally, but for each pixel, it offsets its color value with a corresponding value from the threshold map according to its location, causing the pixel's value to be quantized to a different color if it exceeds the threshold. For most dithering purposes, it is sufficient to simply add the threshold value to every pixel (without performing normalization by subtracting 1⁄2), or equivalently, to compare the pixel's value to the threshold: if the brightness value of a pixel is less than the number in the corresponding cell of the matrix, plot that pixel black, otherwise, plot it white. This lack of normalization slightly increases the average brightness of the image, and causes almost-white pixels to not be dithered. This is not a problem when using a gray scale palette (or any palette where the relative color distances are (nearly) constant), and it is often even desired, since the human eye perceives differences in darker colors more accurately than lighter ones, however, it produces incorrect results especially when using a small or arbitrary palette, so proper normalization should be preferred. In other words, the algorithm performs the following transformation on each color c of every pixel: c ′ = n e a r e s t _ p a l e t t e _ c o l o r ( c + r × ( M ( x mod n , y mod n ) − 1 / 2 ) ) {\displaystyle c'=\mathrm {nearest\_palette\_color} {\mathopen {}}\left(c+r\times \left(M(x{\bmod {n}},y{\bmod {n}})-1/2\right){\mathclose {}}\right)} where M(i, j) is the threshold map on the i-th row and j-th column, c′ is the transformed color, and r is the amount of spread in color space. Assuming an RGB palette with 23N evenly distanced colors where each color (a triple of red, green and blue values) is represented by an octet from 0 to 255, one would typically choose r ≈ 255 N {\textstyle r\approx {\frac {255}{N}}} . (1⁄2 is again the normalizing term.) Because the algorithm operates on single pixels and has no conditional statements, it is very fast and suitable for real-time transformations. Additionally, because the location of the dithering patterns always stays the same relative to the display frame, it is less prone to jitter than error-diffusion methods, making it suitable for animations. Because the patterns are more repetitive than error-diffusion method, an image with ordered dithering compresses better. Ordered dithering is more suitable for line-art graphics as it will result in straighter lines and fewer anomalies. The values read from the threshold map should preferably scale into the same range as the minimal difference between distinct colors in the target palette. Equivalently, the size of the map selected should be equal to or larger than the ratio of source colors to target colors. For example, when quantizing a 24 bpp image to 15 bpp (256 colors per channel to 32 colors per channel), the smallest map one would choose would be 4×2, for the ratio of 8 (256:32). This allows expressing each distinct tone of the input with different dithering patterns. === A variable palette: pattern dithering === == Non-Bayer approaches == The above thresholding matrix approach describes the Bayer family of ordered dithering algorithms. A number of other algorithms are also known; they generally involve changes in the threshold matrix, which changes the distribution of the "noise" introduced by all kinds of dithering (the difference between the original image and the dithered image). === Halftone === Halftone dithering performs a form of clustered dithering, creating a look similar to halftone patterns, using a specially crafted matrix. === Void and cluster === The Void and cluster algorithm uses a pre-generated blue noise as the matrix for the dithering process. The blue noise matrix keeps the Bayer's good high frequency content, but with a more uniform coverage of all the frequencies involved shows a much lower amount of patterning. The "voids-and-cluster" method gets its name from the matrix generation procedure, where a black image with randomly initialized white pixels is gaussian-blurred to find the brightest and darkest parts, corresponding to voids and clusters. After a few swaps have evenly distributed the bright and dark parts, the pixels are numbered by importance. It takes significant computational resources to generate the blue noise matrix: on a modern computer a 64×64 matrix requires a couple seconds using the original algorithm. This algorithm can be extended to make animated dither masks which also consider the axis of time. This is done by running the algorithm in three dimensions and using a kernel which is a product of a two-dimensional gaussian kernel on the XY plane, and a one-dimensional Gaussian kernel on the Z axis. === Simulated Annealing === Simulated annealing can generate dither masks by starting with a flat histogram and swapping values to optimize a loss function. The loss function controls the spectral properties of the mask, allowing it to make blue noise or noise patterns meant to be filtered by specific filters. The algorithm can also be extended over time for animated dither masks with chosen temporal properties.