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Deep Learning Anti-aliasing
Deep learning anti-aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. DLAA is similar to deep learning super sampling (DLSS) in its anti-aliasing method, with one important differentiation being that DLSS's goal is to increase performance at the cost of image quality, where the main priority of DLAA is improving image quality at the cost of performance, irrelevant of resolution upscaling or downscaling. DLAA is similar to temporal anti-aliasing (TAA) in that they're both spatial anti-aliasing solutions relying on past frame data. Compared to TAA, DLAA is substantially better when it comes to shimmering, flickering, and handling small meshes like wires. DLAA collects game rendering data such as raw low-resolution input, motion vectors, depth buffers, and exposure / brightness information. This Information is then used by DLAA to improve upon its anti-aliasing, with the aim of reducing tem ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Anti-aliasing
In digital signal processing, spatial anti-aliasing is a technique for minimizing the distortion artifacts (aliasing) when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications. Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as black-and-white noise. In signal acquisition and audio, anti-aliasing is often done using an analog anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analog-to-digital converter. In digital photography, optical anti-aliasing filters made of birefringent materials smooth the signal in the spatial optical domain. The anti-a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Flicker (light)
In visual perception, flicker is a human-visible change in luminous flux of an illuminated surface or light source which can be due to fluctuations of the light source itself, or due to external causes such as due to rapid fluctuations in the voltage of the power supply ( power-line flicker) or incompatibility with an external dimmer. Twinkling, also called scintillation, is a generic term for variations in apparent brightness, colour, or position of a distant luminous object viewed through a medium. Flicker exists for other organisms having different perceptual thresholds. Light meters and image sensors can potentially detect flicker at much higher frequency bands than human vision. Shutter speeds used in motion photography can interact with high frequency flicker to produce visual artifacts in the captured imagery that betray flicker that would not otherwise be noted. The spectral sensitivity of the human eye to flicker depends upon the mode of visual perception. Due to the f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Temporal Anti-aliasing
Temporal anti-aliasing (TAA) is a spatial anti-aliasing technique for computer-generated video that combines information from past frames and the current frame to remove jaggies in the current frame. In TAA, each pixel is sampled once per frame but in each frame the sample is at a different location within the pixel. Pixels sampled in past frames are blended with pixels sampled in the current frame to produce an anti-aliased image.Brian Kari, Epic Game"High Quality Temporal Supersampling" TAA compared to MSAA Prior to the development of TAA, MSAA was the dominant anti-aliasing technique. MSAA samples (renders) each pixel multiple times at different locations within the frame and averages the samples to produce the final pixel value. In contrast, TAA samples each pixel only once per frame, but it samples the pixels at a different locations in different frames. This makes TAA faster than MSAA. In parts of the picture without motion, TAA effectively computes MSAA over multiple frames ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Anti-aliasing
In digital signal processing, spatial anti-aliasing is a technique for minimizing the distortion artifacts (aliasing) when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications. Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as black-and-white noise. In signal acquisition and audio, anti-aliasing is often done using an analog anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analog-to-digital converter. In digital photography, optical anti-aliasing filters made of birefringent materials smooth the signal in the spatial optical domain. The anti-a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Learning Super Sampling
Deep learning super sampling (DLSS) is a family of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are exclusive to its RTX line of graphics cards, and available in a number of video games. The goal of these technologies is to allow the majority of the graphics pipeline to run at a lower resolution for increased performance, and then infer a higher resolution image from this that contains the same level of detail as if the image had been rendered at this higher resolution. This allows for higher graphical settings and/or frame rates for a given output resolution, depending on user preference. As of September 2022, the 1st and 2nd generation of DLSS is available on all RTX branded cards from Nvidia in supported titles, while the 3rd generation unveiled at Nvidia's GTC 2022 event is exclusive to Ada Lovelace generation RTX 4000 series graphics cards. Nvidia has also introduced Deep learning dynamic super resolution (DLDSR), a related and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”). Variants exist, aiming to force the learned representations to assume useful properties. Examples are regularized autoencoders (''Sparse'', ''Denoising'' and ''Contractive''), which are effective in learning representations for subsequent classification tasks, and ''Variational'' autoencoders, with applications as generative models. Autoencoders are applied to many problems, including facial recognition, feature detection, anomaly detection and acquiring the meaning of words. Autoencoders are also generative models which can randomly generate new data that is similar to the input da ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neuro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Box Blur
A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix :\frac\begin 1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1 \end. Due to its property of using equal weights, it can be implemented using a much simpler accumulation algorithm, which is significantly faster than using a sliding-window algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate a Gaussian blur.code doc In the frequency domain, a box blur has zeros and negative components. That is, a sine wave with a period equal to the size of the box will be blurred away entirely, and wavelengths shorter than the size of the box may be phase-reversed, as seen when two bokeh circles touch to f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ghosting (television)
In television, a ghost is a replica of the transmitted image, offset in position, that is superimposed on top of the main image. It is often caused when a TV signal travels by two different paths to a receiving antenna, with a slight difference in timing.Jorma Hyypia, ''Beating TV Interference'', ''Popular Mechanics'' , June 1980 page 126 Analog ghosting Common causes of ghosts (in the more specific sense) are: * Mismatched impedance along the communication channel, which causes unwanted reflections. The technical term for this phenomenon is ringing. * Multipath distortion, because radio frequency waves may take paths of different length (by reflecting from buildings, transmission lines, aircraft, clouds, etc.) to reach the receiver. In addition, RF leaks may allow a signal to enter the set by a different path; this is most common in a large building such as a tower block or hotel where one TV antenna feeds many different rooms, each fitted with a TV aerial socket (kno ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nvidia
Nvidia CorporationOfficially written as NVIDIA and stylized in its logo as VIDIA with the lowercase "n" the same height as the uppercase "VIDIA"; formerly stylized as VIDIA with a large italicized lowercase "n" on products from the mid 1990s to early-mid 2000s. Though unofficial, second letter capitalization of NVIDIA, i.e. nVidia, may be found within enthusiast communities and publications. ( ) is an American multinational technology company incorporated in Delaware and based in Santa Clara, California. It is a software and fabless company which designs graphics processing units (GPUs), application programming interface (APIs) for data science and high-performance computing as well as system on a chip units (SoCs) for the mobile computing and automotive market. Nvidia is a global leader in artificial intelligence hardware and software. Its professional line of GPUs are used in workstations for applications in such fields as architecture, engineering and construction, media ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Heuristic
A heuristic (; ), or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples that employ heuristics include using trial and error, a rule of thumb or an educated guess. Heuristics are the strategies derived from previous experiences with similar problems. These strategies depend on using readily accessible, though loosely applicable, information to control problem solving in human beings, machines and abstract issues. When an individual applies a heuristic in practice, it generally performs as expected. However it can alternatively cre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
