Lossy compression of Landsat multispectral images

Kozhemiakin, Ruslan; Abramov, Sergey; Djurovic, Blazo; Djurović, Igor; Vozel, Benôıt

doi:10.1109/meco.2016.7525714

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…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). In this case, a reasonable solution is to exploit this correlation for improving the data decorrelation and coder performance [15,16,19,[21][22][23]. However, there are different ways to implement this idea and, respectively, the performance of the corresponding techniques is different.…”

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

“…Note that similar signal power normalizing operations can be useful in lossy compression of multichannel ECG as data pre-processing step [15,27]. Component images grouping and the use of 3D compression for those grouped component images produces CR improvement in lossy compression of multispectral images as well [23].…”

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

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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%

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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“…The performance of the proposed method is comparatively better than JPEG. Kozhemiakin et al 15 proposed a compression method based on 3-D AGU coder that calculates cross-correlation factor for images in different channels. Frequency coefficients are obtained from 3-D DCT where quantization step is set proportional to noise standard deviation.…”

Section: Transform Algorithmsmentioning

confidence: 99%

“…The advantages, limitations, and future directions of each algorithm are listed in Table 1. Kozhemia-kin et al 15 Percentage of zeros obtained after quantization of DCT coefficients can predict improvement of CR due to combination of channels into a group.…”

Section: Transform Algorithmsmentioning

confidence: 99%

Comprehensive review of hyperspectral image compression algorithms

2020

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Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives.

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“…al. [208] indicated that while remote sensing images are widely compressed using lossless compression to preserve the image quality, small compression ratio is achieved and suggested lossy image compression technique based on discrete cosine transform.…”

Section: Introductionmentioning

confidence: 99%

Image compression techniques: A survey in lossless and lossy algorithms

2018

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The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System.

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Lossy compression of Landsat multispectral images

Cited by 7 publications

References 13 publications

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Comprehensive review of hyperspectral image compression algorithms

Image compression techniques: A survey in lossless and lossy algorithms

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