Concept
Compression de données
Concepts associés (51)
Transform coding
Transform coding is a type of data compression for "natural" data like audio signals or photographic s. The transformation is typically lossless (perfectly reversible) on its own but is used to enable better (more targeted) quantization, which then results in a lower quality copy of the original input (lossy compression). In transform coding, knowledge of the application is used to choose information to discard, thereby lowering its bandwidth. The remaining information can then be compressed via a variety of methods.
Modified discrete cosine transform
The modified discrete cosine transform (MDCT) is a transform based on the type-IV discrete cosine transform (DCT-IV), with the additional property of being lapped: it is designed to be performed on consecutive blocks of a larger dataset, where subsequent blocks are overlapped so that the last half of one block coincides with the first half of the next block. This overlapping, in addition to the energy-compaction qualities of the DCT, makes the MDCT especially attractive for signal compression applications, since it helps to avoid artifacts stemming from the block boundaries.
Taux de compression de données
Le taux de compression est une mesure de la performance d'un algorithme de compression de données informatiques. Il est généralement exprimé en pourcentage et noté τ. Deux définitions sont communément admises : L'une définit le taux de compression comme le rapport du volume des données après compression sur le volume initial des données. De ce fait, plus le taux de compression est faible, plus la taille du fichier compressé résultant est faible. Le taux de compression ainsi défini est donné par la formule : τ = [Volume final] / [Volume initial].
Transparency (data compression)
In data compression and psychoacoustics, transparency is the result of lossy data compression accurate enough that the compressed result is perceptually indistinguishable from the uncompressed input, i.e. perceptually lossless. A transparency threshold is a given value at which transparency is reached. It is commonly used to describe compressed data bitrates. For example, the transparency threshold for MP3 to linear PCM audio is said to be between 175 and 245 kbit/s, at 44.
Théorème du codage de source
Le théorème du codage de source (ou premier théorème de Shannon, ou encore théorème de codage sans bruit) est un théorème en théorie de l'information, énoncé par Claude Shannon en 1948, qui énonce la limite théorique pour la compression d'une source. Le théorème montre que l'on ne peut pas compresser une chaine de variables aléatoires i.i.d, quand la longueur de celle-ci tend vers l'infini, de telle sorte à ce que la longueur moyenne des codes des variables soit inférieure à l'entropie de la variable source.
Generation loss
Generation loss is the loss of quality between subsequent copies or transcodes of data. Anything that reduces the quality of the representation when copying, and would cause further reduction in quality on making a copy of the copy, can be considered a form of generation loss. File size increases are a common result of generation loss, as the introduction of artifacts may actually increase the entropy of the data through each generation.
Rate–distortion theory
Rate–distortion theory is a major branch of information theory which provides the theoretical foundations for lossy data compression; it addresses the problem of determining the minimal number of bits per symbol, as measured by the rate R, that should be communicated over a channel, so that the source (input signal) can be approximately reconstructed at the receiver (output signal) without exceeding an expected distortion D. Rate–distortion theory gives an analytical expression for how much compression can be achieved using lossy compression methods.
Discrete wavelet transform
In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time). Haar wavelet The first DWT was invented by Hungarian mathematician Alfréd Haar. For an input represented by a list of numbers, the Haar wavelet transform may be considered to pair up input values, storing the difference and passing the sum.
ITU-T
The International Telecommunication Union Telecommunication Standardization Sector (ITU-T) is one of the three Sectors (branches) of the International Telecommunication Union (ITU). It is responsible for coordinating standards for telecommunications and Information Communication Technology, such as X.509 for cybersecurity, Y.3172 and Y.3173 for machine learning, and H.264/MPEG-4 AVC for video compression, between its Member States, Private Sector Members, and Academia Members.
Variable bitrate
Variable bitrate (ou Variable bit rate, ou encore VBR), est un terme anglais que l'on peut traduire en français par : « taux d'échantillonnage variable », en opposition au constant bitrate (CBR). Lors de la numérisation d'un signal, l'utilisation d'un taux (ou d'une fréquence) d'échantillonnage variable consiste à adapter le nombre d'échantillons prélevés sur le signal, à la complexité locale (ou instantanée) de celui-ci. Ceci s'oppose à la technique plus simple, Constant bit rate ou CBR, dans laquelle le taux d'échantillonnage est fixe.