Lossy compression of noisy images

Al-Shaykh, O.K.; Mersereau, R.M.

doi:10.1109/83.730376

Cited by 132 publications

(72 citation statements)

References 29 publications

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“…El principio general sobre el cual se sustenta la codificación con pérdidas, es que no resulta necesario codificar aquellas componentes de la información que no son observables por los sistemas de percepción humana. La principal ventaja de estas estrategias de compresión es que consiguen factores de compresión muy superiores a los que se obtienen con los métodos sin pérdidas (49,50).…”

Section: Compresión Con Pérdidaunclassified

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Compresión de imágenes médicas

Noreña

Romero

2012

biomedica

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Contribución de los autores:Ambos autores participaron en la recopilación, unificación y síntesis de los antecedentes más destacados en el área de la compresión, mediante la discusión de la información publicada sobre el tema. Además, estructuraron las metodologías ya existentes para la compresión de imágenes médicas, aportando un guía base para las personas que desean introducirse en el área. La medicina moderna es una actividad cada vez más compleja, basada en la información proveniente de múltiples fuentes: historias clínicas, dictáfonos e imágenes y vídeos provenientes de múltiples dispositivos. Las imágenes médicas constituyen una de las fuentes de mayor importancia, por cuanto ofrecen un apoyo integral del acto médico: el diagnóstico y el seguimiento. Sin embargo, la cantidad de información generada por los dispositivos de adquisición de imágenes sobrepasa rápidamente la disponibilidad de almacenamiento que tienen los servicios de radiología, lo cual genera costos adicionales en equipos de cómputo con mayor capacidad de almacenamiento. Además, la tendencia actual de desarrollo de aplicaciones en la "nube de cómputo", tiene limitaciones por cuanto, aunque el almacenamiento es virtual y está disponible desde cualquier sitio, la conexión se hace a través de internet. En estos dos casos, el uso óptimo de la información requiere necesariamente de algoritmos de compresión potentes y adaptados a las necesidades de la actividad médica. En este artículo se presenta una revisión de las técnicas de compresión más utilizadas para el almacenamiento de imágenes, así como un análisis crítico de éstas desde el punto de vista de su uso en ambientes clínicos. REVISIÓN DE TEMAPalabras clave: procesamiento de imagen asistida por computador, compresión de datos, metodologías computacionales.doi: http://dx.doi.org/10.7705/biomedica.v33i1.804 Medical image compression: a reviewModern medicine is an increasingly complex activity, based on the evidence; it consists of information from multiple sources: medical record text, sound recordings, images and videos generated by a large number of devices. Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up. However, the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services, generating additional costs in devices with greater storage capacity. Besides, the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet. In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs. In this paper we present a review of compression techniques used for image storage, and a critical analysis of them from the point of view of their use in clinical settings. Las imágenes médicas son hoy en día parte fundamental de la exploración diagnóstica de un paci...

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Section: Compresión Con Pérdidaunclassified

“…Algunos trabajos (49,66,67) nos permiten concluir que las técnicas de compresión vector de cuantificación y la pirámide de Laplace hacen posible detectar y caracterizar anormalidades mucho más eficientemente en las imágenes de mamografía. Ambas técnicas deben su eficiencia de compresión a la uniformidad presente en los datos médicos.…”

Section: Conclusionesunclassified

Compresión de imágenes médicas

Noreña

Romero

2012

biomedica

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“…In the cases of low bitrates ( C R <I), there are possibilities to tune a full scheme of noisy image compression if to determine so-called optimal operation point (OOP) [4,5] of a coder. It has also been experimentally proved by the author for a case of multiplicative noise that any coder has such bit rate (i.e.…”

Section: The Fusion Of Thresholding Rulesmentioning

confidence: 99%

“…Here, the final compression ratio is determined by the achieved maximal quality of the decompressed image. The second set of methods presented by works [4,5] exploits the idea of so-called optimal operation point (OOP) which is the compression ratio or bit rate (BR) measured in bit per pixel (bpp), when the maximal peak-to-signal noise ratio (PSNR) is achieved. Existence of OOP can be explained by existence of so-called "dead zone", an interval of quantization near zero, size of which is changed in dependency on the given bit rate for any codec.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

et al. 2016

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Abstract. The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

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“…[1]. Some acquired images need a special pre-processing (e.g., denoising, deblurring, edge detection, compression [2][3][4], etc.) for further exploitation (e.g., object recognition, visual inspection, diagnostics, etc.).…”

Section: Introductionmentioning

confidence: 99%

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

et al. 2014

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Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise variance estimation for the cases of additive and multiplicative noise models. However, more complicated noise models containing a mixture of signal-independent (SI) and signal-dependent (SD) components are often more adequate in practice. Parameters of both components have to be automatically estimated to be used in image enhancement. This paper addresses a question of required accuracy of such estimation. Analysis is carried out for color images processed by a filter based on discrete cosine transform. The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency. This efficiency is characterized by the conventional criterion peak signal-to-noise ratio (PSNR) and two visual quality metrics, PSNR human visual system masking (PSNR-HVS-M) and multi-scale structural similarity (MSSIM). If a reduction of filtering efficiency exceeds 0.5 dB (in terms of PSNR and PSNR-HVS-M) or 0.005 (in terms of MSSIM), mixed noise parameters estimation is assumed to be unacceptable. As the result, it is shown that SI and SD noise parameters have to be estimated with a relative error not exceeding 20%…30%.

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Lossy compression of noisy images

Cited by 132 publications

References 29 publications

Compresión de imágenes médicas

Compresión de imágenes médicas

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

On required accuracy of mixed noise parameter estimation for image enhancement via denoising

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