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29-456: JPEG XR ( JPEG extended range ) is an image compression standard for continuous tone photographic images, based on the HD Photo (formerly Windows Media Photo ) specifications that Microsoft originally developed and patented. It supports both lossy and lossless compression , and is the preferred image format for Ecma-388 Open XML Paper Specification documents. Support for the format

58-487: A Plug-in : The following APIs and software frameworks support JPEG XR and may be used in other software to provide JPEG XR support to end users: The 2011 video game Rage employs JPEG XR compression to compress its textures . .jxr are images created for hdr screenshots by [ xbox gamebar , nvidia shadowplay , obs , special k ]. once the image is out of it s original .jxr container, no software exists to remux back to .jxr again (jxrencapp.exe from jxrlib corrupts

87-541: A corrigendum approved in December 2009, and ISO/IEC issued a new edition with similar corrections on 30 September 2010. In 2010, after completion of the image coding specification, the ITU-T and ISO/IEC also published a motion format specification (ITU-T T.833 | ISO/IEC 29199-3), a conformance test set (ITU-T T.834 | ISO/IEC 29199-4), and reference software (ITU-T T.835 | ISO/IEC 29199-5) for JPEG XR. In 2011, they published

116-530: A Motion JPEG XR specification was approved as an ISO standard for motion (video) compression in March 2010. JPEG XR's design is conceptually very similar to JPEG : the source image is optionally converted to a luma-chroma colorspace, the chroma planes are optionally subsampled , each plane is divided into fixed-size blocks, the blocks are transformed into the frequency domain, and the frequency coefficients are quantized and entropy coded . Major differences include

145-423: A compression method often is measured by the peak signal-to-noise ratio . It measures the amount of noise introduced through a lossy compression of the image, however, the subjective judgment of the viewer also is regarded as an important measure, perhaps, being the most important measure. Entropy coding started in the late 1940s with the introduction of Shannon–Fano coding , the basis for Huffman coding which

174-406: A quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression). Other names for scalability are progressive coding or embedded bitstreams . Despite its contrary nature, scalability also may be found in lossless codecs, usually in form of coarse-to-fine pixel scans. Scalability is especially useful for previewing images while downloading them (e.g., in

203-728: A single compressed codestream.) Being TIFF-based, this format inherits all of the limitations of the TIFF format including the 4 GB file-size limit, which according to the HD Photo specification "will be addressed in a future update". New work has been started in the JPEG committee to enable the use of JPEG XR image coding within the JPX file storage format – enabling use of the JPIP protocol, which allows interactive browsing of networked images. Additionally,

232-773: A technical report describing the workflow architecture for the use of JPEG XR images in applications (ITU-T T.Sup2 | ISO/IEC TR 29199-1). JPEG XR is an image file format that offers several key improvements over JPEG , including: One file container format that can be used to store JPEG XR image data is specified in Annex A of the JPEG XR standard. It is a TIFF -like format using a table of Image File Directory (IFD) tags. A JPEG XR file contains image data, optional alpha channel data, metadata, optional XMP metadata stored as RDF/XML , and optional Exif metadata, in IFD tags. The image data

261-751: A web browser) or for providing variable quality access to e.g., databases. There are several types of scalability: Region of interest coding . Certain parts of the image are encoded with higher quality than others. This may be combined with scalability (encode these parts first, others later). Meta information . Compressed data may contain information about the image which may be used to categorize, search, or browse images. Such information may include color and texture statistics, small preview images, and author or copyright information. Processing power . Compression algorithms require different amounts of processing power to encode and decode. Some high compression algorithms require high processing power. The quality of

290-411: Is a contiguous self-contained chunk of data. The optional alpha channel, if present, can be compressed as a separate image record, enabling decoding of the image data independently of transparency data in applications which do not support transparency. (Alternatively, JPEG XR also supports an "interleaved" alpha channel format in which the alpha channel data is encoded together with the other image data in

319-401: Is a fundamental technique used in image compression algorithms to achieve efficient data representation. Named after its inventor David A. Huffman, this method is widely employed in various image compression standards such as JPEG and PNG. Huffman coding is a form of entropy encoding that assigns variable-length codes to input symbols based on their frequencies of occurrence. The basic principle

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348-442: Is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless. Methods for lossy compression : Methods for lossless compression : The best image quality at a given compression rate (or bit rate ) is the main goal of image compression, however, there are other important properties of image compression schemes: Scalability generally refers to

377-833: Is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem , which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source. More precisely, the source coding theorem states that for any source distribution, the expected code length satisfies E x ∼ P ⁡ [ ℓ ( d ( x ) ) ] ≥ E x ∼ P ⁡ [ − log b ⁡ ( P ( x ) ) ] {\displaystyle \operatorname {E} _{x\sim P}[\ell (d(x))]\geq \operatorname {E} _{x\sim P}[-\log _{b}(P(x))]} , where ℓ {\displaystyle \ell }

406-456: Is the number of symbols in a code word, d {\displaystyle d} is the coding function, b {\displaystyle b} is the number of symbols used to make output codes and P {\displaystyle P} is the probability of the source symbol. An entropy coding attempts to approach this lower bound. Two of the most common entropy coding techniques are Huffman coding and arithmetic coding . If

435-426: Is to assign shorter codes to more frequently occurring symbols and longer codes to less frequent symbols, thereby reducing the average code length compared to fixed-length codes. In image compression, Huffman coding is typically applied after other transformations like Discrete Cosine Transform (DCT) in the case of JPEG compression. After transforming the image data into a frequency domain representation, Huffman coding

464-655: Is used in the GIF format, introduced in 1987. DEFLATE , a lossless compression algorithm developed by Phil Katz and specified in 1996, is used in the Portable Network Graphics (PNG) format. The JPEG 2000 standard was developed from 1997 to 2000 by a JPEG committee chaired by Touradj Ebrahimi (later the JPEG president). In contrast to the DCT algorithm used by the original JPEG format, JPEG 2000 instead uses discrete wavelet transform (DWT) algorithms. It uses

493-547: Is used to encode the transformed coefficients efficiently. Huffman coding plays a crucial role in image compression by efficiently encoding image data into a compact representation. Its ability to adaptively assign variable-length codewords based on symbol frequencies makes it an essential component in modern image compression techniques, contributing to the reduction of storage space and transmission bandwidth while maintaining image quality. Entropy encoding In information theory , an entropy coding (or entropy encoding )

522-601: The CDF 9/7 wavelet transform (developed by Ingrid Daubechies in 1992) for its lossy compression algorithm, and the Le Gall–Tabatabai (LGT) 5/3 wavelet transform (developed by Didier Le Gall and Ali J. Tabatabai in 1988) for its lossless compression algorithm. JPEG 2000 technology, which includes the Motion JPEG 2000 extension, was selected as the video coding standard for digital cinema in 2004. Huffman coding

551-558: The Joint Photographic Experts Group (JPEG) in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format . JPEG was largely responsible for the wide proliferation of digital images and digital photos , with several billion JPEG images produced every day as of 2015. Lempel–Ziv–Welch (LZW) is a lossless compression algorithm developed by Abraham Lempel , Jacob Ziv and Terry Welch in 1984. It

580-572: The Joint Photographic Experts Group and Microsoft announced HD Photo to be under consideration to become a JPEG standard known as JPEG XR . On 16 March 2009, JPEG XR was given final approval as ITU-T Recommendation T.832 and starting in April 2009, it became available from the ITU-T in "pre-published" form. On 19 June 2009, it passed an ISO/IEC Final Draft International Standard (FDIS) ballot, resulting in final approval as International Standard ISO/IEC 29199-2 . The ITU-T updated its publication with

609-461: The approximate entropy characteristics of a data stream are known in advance (especially for signal compression ), a simpler static code may be useful. These static codes include universal codes (such as Elias gamma coding or Fibonacci coding ) and Golomb codes (such as unary coding or Rice coding ). Since 2014, data compressors have started using the asymmetric numeral systems family of entropy coding techniques, which allows combination of

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638-425: The compression ratio of arithmetic coding with a processing cost similar to Huffman coding . Besides using entropy coding as a way to compress digital data, an entropy encoder can also be used to measure the amount of similarity between streams of data and already existing classes of data. This is done by generating an entropy coder/compressor for each class of data; unknown data is then classified by feeding

667-510: The file, and the other apis on this list when possible to export in .jxr corrupts the file as well). Microsoft has patents on the technology in JPEG XR. A Microsoft representative stated in a January 2007 interview that in order to encourage the adoption and use of HD Photo, the specification is made available under the Microsoft Open Specification Promise , which asserts that Microsoft allows implementation of

696-690: The following: The HD Photo bitstream specification claims that "HD Photo offers image quality comparable to JPEG-2000 with computational and memory performance more closely comparable to JPEG", that it "delivers a lossy compressed image of better perceptive quality than JPEG at less than half the file size", and that "lossless compressed images … are typically 2.5 times smaller than the original uncompressed data". A reference software implementation of JPEG XR has been published as ITU-T Recommendation T.835 and ISO/IEC International Standard 29199-5. The following notable software products natively support JPEG XR: The following notable software support JPEG XR through

725-534: The list of specifications covered by its Community Promise . In April 2013, Microsoft released jxrlib, an open source JPEG XR library under the BSD licence . This resolved any licensing issues with the library being implemented in software packages distributed under popular open source licences such as the GNU General Public License , with which the previously released "HD Photo Device Porting Kit"

754-492: The specification for free, and will not file suits on the patented technology for its implementation, as reportedly stated by Josh Weisberg, director of Microsoft's Rich Media Group. As of 15 August 2010, Microsoft made the resulting JPEG XR standard available under its Community Promise . In July 2010, reference software to implement the JPEG XR standard was published as ITU-T Recommendation T.835 and International Standard ISO/IEC 29199-5. Microsoft included these publications in

783-478: Was incompatible. Image compression Image compression may be lossy or lossless . Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art , or comics. Lossy compression methods, especially when used at low bit rates , introduce compression artifacts . Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity

812-625: Was made available in Adobe Flash Player 11.0, Adobe AIR 3.0, Sumatra PDF 2.1, Windows Imaging Component , .NET Framework 3.0, Windows Vista , Windows 7 , Windows 8 , Internet Explorer 9 , Internet Explorer 10 , Internet Explorer 11 , Pale Moon 27.2. As of January 2021, there were still no cameras that shoot photos in the JPEG XR (.JXR) format. Microsoft first announced Windows Media Photo at WinHEC 2006, and then renamed it to HD Photo in November of that year. In July 2007,

841-464: Was published in 1952. Transform coding dates back to the late 1960s, with the introduction of fast Fourier transform (FFT) coding in 1968 and the Hadamard transform in 1969. An important development in image data compression was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed , T. Natarajan and K. R. Rao in 1973. JPEG was introduced by

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