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JPEG2000 JPEG JPEG 2000 (JP2) is an image compression standard and coding system. It was created by the Joint Photographic Experts Group committee in 2000 with the intention of superseding their original discrete cosine transformbased JPEG JPEG standard (created in 1992) with a newly designed, waveletbased method. The standardized filename extension is .jp2 for ISO/IEC 154441 conforming files and .jpx for the extended part2 specifications, published as ISO/IEC 154442. The registered MIME types are defined in RFC 3745. For ISO/IEC 154441 it is image/jp2. JPEG JPEG 2000 code streams are regions of interest that offer several mechanisms to support spatial random access or region of interest access at varying degrees of granularity [...More...]  "JPEG2000" on: Wikipedia Yahoo 

Media Type A media type (also MIME type and content type)[1] is a twopart identifier for file formats and format contents transmitted on the Internet. The Internet Internet Assigned Numbers Authority (IANA) is the official authority for the standardization and publication of these classifications. Media types were originally defined in Request for Comments 2045 in November 1996 as a part of MIME (Multipurpose Internet Internet Mail Extensions) specification, for denoting type of email message content and attachments;[2] hence the name MIME type. Media types are also used by other internet protocols such as HTTP[3] and document file formats such as HTML,[4] for similar purpose.Contents1 Naming1.1 Common examples 1.2 Registration trees1.2.1 Standards tree 1.2.2 Vendor tree 1.2.3 Personal or Vanity tree 1.2.4 Unregistered x [...More...]  "Media Type" on: Wikipedia Yahoo 

Bit Plane A bit plane of a digital discrete signal (such as image or sound) is a set of bits corresponding to a given bit position in each of the binary numbers representing the signal.[1] For example, for 16bit data representation there are 16 bit planes: the first bit plane contains the set of the most significant bit, and the 16th contains the least significant bit. It is possible to see that the first bit plane gives the roughest but the most critical approximation of values of a medium, and the higher the number of the bit plane, the less is its contribution to the final stage. Thus, adding a bit plane gives a better approximation. If a bit on the nth bit plane on an mbit dataset is set to 1, it contributes a value of 2(mn), otherwise it contributes nothing. Therefore, bit planes can contribute half of the value of the previous bit plane [...More...]  "Bit Plane" on: Wikipedia Yahoo 

Color Space A color space is a specific organization of colors. In combination with physical device profiling, it allows for reproducible representations of color, in both analog and digital representations. A color space may be arbitrary, with particular colors assigned to a set of physical color swatches and corresponding assigned names or numbers such as with the Pantone Pantone collection, or structured mathematically, as with NCS System, Adobe RGB or sRGB. A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers (e.g. triples in RGB or quadruples in CMYK); however, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with no connection to any globally understood system of color interpretation [...More...]  "Color Space" on: Wikipedia Yahoo 

YCbCr YCbCr, Y′CbCr, or Y Pb/Cb Pr/Cr, also written as YCBCR or Y'CBCR, is a family of color spaces used as a part of the color image pipeline in video and digital photography systems. Y′ is the luma component and CB and CR are the bluedifference and reddifference chroma components. Y′ (with prime) is distinguished from Y, which is luminance, meaning that light intensity is nonlinearly encoded based on gamma corrected RGB RGB primaries. Y′CbCr color spaces are defined by a mathematical coordinate transformation from an associated RGB RGB color space [...More...]  "YCbCr" on: Wikipedia Yahoo 

Chrominance Chrominance Chrominance (chroma or C for short) is the signal used in video systems to convey the color information of the picture, separately from the accompanying luma signal (or Y for short). Chrominance Chrominance is usually represented as two colordifference components: U = B′ − Y′ (blue − luma) and V = R′ − Y′ (red − luma). Each of these difference components may have scale factors and offsets applied to it, as specified by the applicable video standard. In composite video signals, the U and V signals modulate a color subcarrier signal, and the result is referred to as the chrominance signal; the phase and amplitude of this modulated chrominance signal correspond approximately to the hue and saturation of the color [...More...]  "Chrominance" on: Wikipedia Yahoo 

RGB Color Model The RGB color model RGB color model is an additive color model in which red, green and blue light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three additive primary colors, red, green, and blue. The main purpose of the RGB color model RGB color model is for the sensing, representation and display of images in electronic systems, such as televisions and computers, though it has also been used in conventional photography. Before the electronic age, the RGB color model already had a solid theory behind it, based in human perception of colors. RGB is a devicedependent color model: different devices detect or reproduce a given RGB value differently, since the color elements (such as phosphors or dyes) and their response to the individual R, G, and B levels vary from manufacturer to manufacturer, or even in the same device over time [...More...]  "RGB Color Model" on: Wikipedia Yahoo 

Lifting Scheme The lifting scheme is a technique for both designing wavelets and performing the discrete wavelet transform (DWT). In an implementation, it is often worthwhile to merge these steps and design the wavelet filters while performing the wavelet transform. This is then called the secondgeneration wavelet transform. The technique was introduced by Wim Sweldens.[1] The lifting scheme factorizes any discrete wavelet transform with finite filters into a series of elementary convolution operators, socalled lifting steps, which reduces the number of arithmetic operations by nearly a factor two. Treatment of signal boundaries is also simplified.[2] The discrete wavelet transform applies several filters separately to the same signal. In contrast to that, for the lifting scheme, the signal is divided like a zipper [...More...]  "Lifting Scheme" on: Wikipedia Yahoo 

Convolution In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g) to produce a third function, that is typically viewed as a modified version of one of the original functions, giving the integral of the pointwise multiplication of the two functions as a function of the amount that one of the original functions is translated[clarification needed]. Convolution Convolution is similar to crosscorrelation. For discrete real valued signals, they differ only in a time reversal in one of the signals. For continuous signals, the crosscorrelation operator is the adjoint operator of the convolution operator. It has applications that include probability, statistics, computer vision, natural language processing, image and signal processing, engineering, and differential equations[citation needed]. The convolution can be defined for functions on groups other than Euclidean space[citation needed] [...More...]  "Convolution" on: Wikipedia Yahoo 

Quantization (image Processing) Quantization, involved in image processing, is a lossy compression technique achieved by compressing a range of values to a single quantum value. When the number of discrete symbols in a given stream is reduced, the stream becomes more compressible. For example, reducing the number of colors required to represent a digital image makes it possible to reduce its file size. Specific applications include DCT data quantization in JPEG JPEG and DWT data quantization in JPEG JPEG 2000.Contents1 Color quantization 2 Frequency quantization for image compression2.1 Quantization matrices3 See also 4 ReferencesColor quantization[edit] Main article: Color quantization Color quantization Color quantization reduces the number of colors used in an image; this is important for displaying images on devices that support a limited number of colors and for efficiently compressing certain kinds of images [...More...]  "Quantization (image Processing)" on: Wikipedia Yahoo 

Real Numbers In mathematics, a real number is a value that represents a quantity along a line. The adjective real in this context was introduced in the 17th century by René Descartes, who distinguished between real and imaginary roots of polynomials. The real numbers include all the rational numbers, such as the integer −5 and the fraction 4/3, and all the irrational numbers, such as √2 (1.41421356..., the square root of 2, an irrational algebraic number). Included within the irrationals are the transcendental numbers, such as π (3.14159265...). Real numbers can be thought of as points on an infinitely long line called the number line or real line, where the points corresponding to integers are equally spaced. Any real number can be determined by a possibly infinite decimal representation, such as that of 8.632, where each consecutive digit is measured in units one tenth the size of the previous one [...More...]  "Real Numbers" on: Wikipedia Yahoo 

Arithmetic Coding Arithmetic coding Arithmetic coding is a form of entropy encoding used in lossless data compression. Normally, a string of characters such as the words "hello there" is represented using a fixed number of bits per character, as in the ASCII code. When a string is converted to arithmetic encoding, frequently used characters will be stored with fewer bits and notsofrequently occurring characters will be stored with more bits, resulting in fewer bits used in total. Arithmetic coding Arithmetic coding differs from other forms of entropy encoding, such as Huffman coding, in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, an arbitraryprecision fraction q where 0.0 ≤ q < 1.0. It represents the current information as a range, defined by two numbers [...More...]  "Arithmetic Coding" on: Wikipedia Yahoo 

Lossy Compression In information technology, lossy compression or irreversible compression is the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storage, handling, and transmitting content. Different versions of the photo of the cat above show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression (reversible data compression) which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than through lossless techniques. Welldesigned lossy compression technology often reduces file sizes significantly before degradation is noticed by the enduser [...More...]  "Lossy Compression" on: Wikipedia Yahoo 

Rate–distortion Optimization Ratedistortion optimization (RDO) is a method of improving video quality in video compression. The name refers to the optimization of the amount of distortion (loss of video quality) against the amount of data required to encode the video, the rate. While it is primarily used by video encoders, ratedistortion optimization can be used to improve quality in any encoding situation (image, video, audio, or otherwise) where decisions have to be made that affect both file size and quality simultaneously.Contents1 Background 2 How it works 3 List of encoders that support RDO 4 ReferencesBackground[edit] The classical method of making encoding decisions is for the video encoder to choose the result which yields the highest quality output image. However, this has the disadvantage that the choice it makes might require more bits while giving comparatively little quality benefit [...More...]  "Rate–distortion Optimization" on: Wikipedia Yahoo 

Lagrange Multiplier In mathematical optimization, the method of Lagrange multipliers (named after JosephLouis Lagrange[1]) is a strategy for finding the local maxima and minima of a function subject to equality constraints. For the case of only one constraint and only two choice variables (as exemplified in Figure 1), consider the optimization problemmaximize f(x, y) subject to g(x, y) = 0.We assume that both f and g have continuous first partial derivatives. We introduce a new variable (λ) called a Lagrange multiplier Lagrange multiplier and study the Lagrange function (or Lagrangian or Lagrangian expression) defined [...More...]  "Lagrange Multiplier" on: Wikipedia Yahoo 

JPEG XR JPEG JPEG XR[4] ( JPEG JPEG extended range[5]) is a stillimage compression standard and file format for continuous tone ph [...More...]  "JPEG XR" on: Wikipedia Yahoo 