HOME TheInfoList.com
Providing Lists of Related Topics to Help You Find Great Stuff
[::MainTopicLength::#1500] [::ListTopicLength::#1000] [::ListLength::#15] [::ListAdRepeat::#3]

picture info

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 transform-based JPEG
JPEG
standard (created in 1992) with a newly designed, wavelet-based method. The standardized filename extension is .jp2 for ISO/IEC 15444-1 conforming files and .jpx for the extended part-2 specifications, published as ISO/IEC 15444-2. The registered MIME types are defined in RFC 3745. For ISO/IEC 15444-1 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
Google
Yahoo

Media Type
A media type (also MIME type and content type)[1] is a two-part 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
Google
Yahoo

picture info

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 16-bit 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 m-bit dataset is set to 1, it contributes a value of 2(m-n), otherwise it contributes nothing. Therefore, bit planes can contribute half of the value of the previous bit plane
[...More...]

"Bit Plane" on:
Wikipedia
Google
Yahoo

picture info

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
Google
Yahoo

picture info

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 blue-difference and red-difference 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
Google
Yahoo

picture info

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 color-difference 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
Google
Yahoo

picture info

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 device-dependent 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
Google
Yahoo

picture info

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 second-generation 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, so-called 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
Google
Yahoo

picture info

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 cross-correlation. For discrete real valued signals, they differ only in a time reversal in one of the signals. For continuous signals, the cross-correlation 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
Google
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
Google
Yahoo

picture info

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
Google
Yahoo

picture info

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 not-so-frequently 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 arbitrary-precision 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
Google
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. Well-designed lossy compression technology often reduces file sizes significantly before degradation is noticed by the end-user
[...More...]

"Lossy Compression" on:
Wikipedia
Google
Yahoo

Rate–distortion Optimization
Rate-distortion 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, rate-distortion 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
Google
Yahoo

picture info

Lagrange Multiplier
In mathematical optimization, the method of Lagrange multipliers (named after Joseph-Louis 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
Google
Yahoo

picture info

JPEG XR
JPEG
JPEG
XR[4] ( JPEG
JPEG
extended range[5]) is a still-image compression standard and file format for continuous tone ph
[...More...]

"JPEG XR" on:
Wikipedia
Google
Yahoo
.