Set redundancy compression
En ciencias de la computación y teoría de la información, set redundancy compression (SRC) son métodos de compresión de datos que explotan redundancias entre grupos de datos individuales de un conjunto, usualmente un conjunto de imágenes similares. Dentro de los campos en los que se usa se destacan las imágenes médicas y satelitales.[1][2][3][4] Los principales métodos son diferencial mín-máx, predictivo mín-máx y el método centroide.
Métodos
editarDiferencial mín-máx
editarEn el método diferencial mín-máx, min-max differential (o MMD por sus sigla en inglés), por cada posición (píxel), se selecciona el valor más alto o el más bajo. Y luego, en cada imagen, se almacena la diferencia de cada una de sus posiciones con respecto al valor previamente seleccionado.
Predictivo mín-máx
editarEn el método predictivo mín-máx, min-max predictive (o MMP): Es un método más complicado, pero más poderoso, que el MMD. En este caso se comienza tomando tanto el valor más bajo como el más alto que puede tomar una posición dentro del grupo. Considerando que cualquier valor en esa posición debe encontrarse dentro de ese rango, se representa la posición dentro de ese rango como "nivel" (level), tal como se describe en la siguiente fórmula:
En las imágenes, los puntos vecinos suelen tener el mismo nivel, o al menos tienden a tener valores cercanos, con mucho menos diferencia que sus valores absolutos. Entonces, el método predictivo se basa en esta característica para predecir que el valor será similar al del punto anterior, o a un punto vecino.
Referencias
editar- ↑ Karadimitriou, Kosmas (agosto de 1996), Set redundancy, the enhanced compression model, andmethods for compressing sets of similar images, «This statistical correlation among similar images is a result of inter-image redundancy. In this study, the term “set redundancy” is introduced to describe this type of redundant information, and is defined as follows: Definition: Set redundancy is the inter-image redundancy that exists in a set of similar images, and refers to the common information found in more than one image in the set. Set redundancy can be used to improve compression. A limit to compression is imposed by the image entropy. In the next section it is shown how set redundancy can be used to decrease the average image entropy in a set of similar images.». Ph.D. thesis, Department of Computer Science, Louisiana State University, Baton Rouge, La, USA
- ↑ Ait-Aoudia, Samy; Gabis, Abdelhalim (27 de febrero de 2005), A Comparison of Set Redundancy Compression Techniques, consultado el 28 de septiembre de 2012, «Medical imaging applications produce a huge amount of similar images. Storing such amount of data needs gigantic disk space. Thus a compression technique is necessary to reduce space storage. In addition, medical images must be stored without any loss of information since the fidelity of images is critical in diagnosis. This requires lossless compression techniques. Lossless compression is an error-free compression. The decompressed image is the same as the original image. Classical image compression techniques (see [1–5]) concentrate on how to reduce the redundancies presented in an individual image. These compression techniques use the same model of compression as shown in Figure 1. Thismodel ignores an additional type of redundancy that exists in sets of similar images, the “set redundancy.” The term “set redundancy” was introduced for the first time by Karadimitriou [6] and defined as follows: “Set redundancy is the interimage redundancy that exists in a set of similar images, and refers to the common information found in more than one image in the set.».
- ↑ Ait-Aoudia, Samy; Gabis, Abdelhalim; Naimi, Amina, Compressing Sets of Similar Images, «Applications using these types of data, produce a large amount of similar images. Thus a compression technique is useful to reduce transmission time and space storage. Lossless compression methods are necessary in such critical applications. Set Redundancy Compression (SRC) methods exploit the interimage redundancy and achieve better results than individual image compression techniques when applied to sets of similar images.».
- ↑ Karadimitriou, Kosmas; Tyler, John M., The Centroid method for compressing sets of similar images, «Karadimitriou (1996) proposed the Enhanced Compression Model as a more appropriate model for compressing sets of similar images. […] Methods that achieve set redundancy reduction are referred to as SRC (Set Redundancy Compression) methods. Two SRC methods are the Min-Max Differential method (Karadimitriou and Tyler, 1996) and the Min-Max Predictive method (Karadimitriou and Tyler, 1997).[…] One of the best application areas for SRC methods is medical imaging. Medical image databases usually store similar images; therefore, they contain large amounts of set redundancy.».
Datos: Q7456298
