A study of different compression algorithms for multispectral images

Vura, Swetha; Patil, Premjyoti; Patil, Shantakumar B.

doi:10.1016/j.matpr.2021.06.175

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Analysis of recent sources The need to compress multichannel signals and images appears in practice of data processing quite often. It is, in particular, typical for multichannel electrocardiogram (ECG) compression [14,15], color image coding [16], compression of dual-polarization radar [17], multispectral [10,[18][19][20], and hyperspectral [11,21] images. A common and typical property of all these types of signals and images is that data in channels (components) are quite highly correlated (at least, for most component data).…”

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

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This paper concerns some aspects of lossy compression applied to six-component Landsat multispectral images where all component images have identical spatial resolution. It is shown that most component images are correlated although the cross-correlation coefficients for component images vary in rather wide limits – from 0.11 in one dataset and 0.72 in another dataset to almost unity in both cases. Compression based on discrete cosine transform is considered where 2D blocks have the size of 32×32 pixels. Embedded deblocking after decompression is applied as well. Lossy compression can be applied component-wise and in a 3D manner for spectral decorrelation of data where 6-point discrete cosine transform is applied. It is shown that, in the latter case, a significantly larger compression ratio can be achieved. The attained benefit in CR is about 30-80% compared to the component-wise compression or, alternatively, the benefit in peak signal-to-noise ratio can reach a few dB for the same compression ratio. The coders are studied for peak signal-to-noise ratio varying in the limits from about 23 dB to about 46 dB that correspond to image quality starting from annoying to practically invisible distortions. In addition, there are some image pre-processing operations that are able to further improve the compression ratio. They are image normalization and band re-ordering. All possible variants of band ordering are studied for both test images. The impact of band ordering on compressed image quality is analyzed in terms of rate/distortion curves for the 2D and 3D versions of the considered coder. It is demonstrated that, due to optimal ordering, a few % improvement of CR can be gained. However, the optimal band order for the considered multichannel images is not the same and this can cause problems in practice to be studied in the future.

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Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

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“…Los escáneres funcionan en regiones concretas del espectro electromagnético. Como las imágenes multiespectrales representan la misma zona espacial de la tierra leyéndola varias veces, la información obtenida se presenta en forma de cubo (Vura et al, 2023). En particular, las características de propagación en la Tierra, y la posibilidad de comunicarse en el espacio a través de satélites a través de la atmósfera, hacen que esta banda de frecuencias sea ideal para una variedad de propósitos de transmisión de información, dando lugar a las radiocomunicaciones (Figura 1).…”

Section: Imágenes Multiespectralesunclassified

Algoritmo de inteligencia artificial para la detección de cultivos de cacao (Teobroma cacao L.), banano (Musa paradisiaca L.) y palma africana (Elaeis guineensis J.).

Zambrano-Garcia,

Vlassova

2023

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Se analiza la expansión e intensificación de la agricultura no regulada e insostenible, principalmente en América Latina, enfocándose en Ecuador, donde la degradación del suelo está estrechamente relacionada con prácticas agrícolas insostenibles. Destaca la importancia de una planificación territorial exhaustiva y el uso de teledetección para evaluar cambios en el uso del suelo y la cobertura vegetal. La investigación se centra en la zona norte de las provincias del Guayas y de Los Ríos en Ecuador, enfocándose en el procesamiento de imágenes satelitales para detectar cultivos de banano, cacao y palma. Utiliza una metodología experimental y cuantitativa para comparar la efectividad de distintas metodologías de clasificación, evaluando la precisión del método de inteligencia artificial Bosques Aleatorios usando imágenes del satélite Sentinel-2. Los resultados del estudio incluyen el preprocesamiento de imágenes satelitales, el cálculo de índices espectrales NDVI y RESI, y la clasificación supervisada de las imágenes de Sentinel-2 utilizando los métodos de Máxima Verosimilitud y Bosques Aleatorios. Se realizaron análisis estadísticos detallados, incluyendo la validación de resultados mediante matrices de confusión y el cálculo del coeficiente Kappa. En conclusión, el manuscrito ofrece una comprensión profunda de cómo la teledetección y la inteligencia artificial pueden ayudar en la detección y clasificación de cultivos en áreas específicas, contribuyendo a la gestión agrícola y la planificación territorial sostenible.

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“…For example, in storage disks, many images need a large amount of storage space and more time for wireless transmission. Therefore, an efficient compression algorithm plays a vital role in saving storage space and transmission time (Siddeq and Rodrigues, 2017;Hameed et al, 2020;Vura et al, 2021). Image compression is a process in which compression is done by encoding image data with fewer bits, and redundant data is removed.…”

Section: Introductionmentioning

confidence: 99%

Improved Median Edge Detection (iMED) for Lossless Image Compression

et al. 2023

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A wide variety of applications are used in lossless image compression models, especially in medical, space, and aerial imaging domains. Predictive coding improves the performance of lossless image compression, which highly relies on entropy error. Lower entropy error results in better image compression. The main focus of this research is to improve the prediction process by minimizing the entropy error. This paper proposes a novel idea for improved Median Edge Detection (iMED) predictor for lossless image compression. MED predictor is improved using k-means clustering and finding the local context of pixels using 20-Dimensional Difference (DDx20) for input images and updates the cluster weights using learning rates (µi) to minimize the prediction errors of pixels. The performance of the proposed predictor is evaluated on the standard grey-scale test images dataset and KODAK image dataset. Results are obtained and compared based on entropy error, bits per pixel (bpp), and computational running time in seconds(s) with the MED, GAP, FLIF, and LBP predictors. The performance of the proposed iMED predictor improves significantly in terms of the entropy error, bpp, and computational running time in seconds(s) after comparison with different state-of-the-art predictors.

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A study of different compression algorithms for multispectral images

Cited by 5 publications

References 19 publications

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Algoritmo de inteligencia artificial para la detección de cultivos de cacao (Teobroma cacao L.), banano (Musa paradisiaca L.) y palma africana (Elaeis guineensis J.).

Improved Median Edge Detection (iMED) for Lossless Image Compression

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