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Compression ratio prediction in lossy compression of noisy images
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015Our paper addresses a question of prediction compression ratio in lossy compression of remote sensing images by coders based on discrete cosine transform (DCT) taking into account noise present in these images. Quantization step is set fixed and proportional to noise standard deviation to provide compression in optimal operation point if it exists ...
Alexander N. Zemliachenko +4 more
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A Lossless-by-Lossy Approach to Lossless Image Compression
2006 International Conference on Image Processing, 2006This paper proposes a method of lossless image coding by the aid of lossy image coding. It aims at an improvement in the compression efficiency. We apply a kind of embedded coding to large coefficients in magnitude in a wavelet transform domain. The other wavelet coefficients are encoded by a context-based entropy coding.
Kazuma Shinoda +2 more
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A generalization of "image lossy data compression" recommendation
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004This paper deals with the encoding of high resolution images for remote sensing and geographic information systems applications. We are currently investigating the suitability of several still image coding techniques for this kind of applications. We present results for an adapted and modified version of the CCSDS-ILDC technique.
Joan Serra-Sagristà +3 more
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Lossy and lossless compression for color-quantized images
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002An efficient compression scheme for color-quantized images based on progressive coding of color information has been developed. Rather than sorting color indexes into a linear list structure, a binary-tree structure of color indexes is proposed. With this structure the new algorithm can progressively recover an image from 2 colors up to all of the ...
Xin Chen, Sam Kwong, Ju-fu Feng
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Fundamentals of Lossy Image Compression
1995Lossy compression of images deals with compression processes where decompression yields an imperfect reconstruction of the original image data. A wide range of lossy compression methods have been developed for compressing still-image data. These methods fall into one of the categories shown in Figure 1.2.
Vasudev Bhaskaran +1 more
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Lossy compression of hyperspectral images based on JPEG2000
2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 2017Lossy compression of images is used in different applications including remote sensing. A problem is how to carry out such lossy compression automatically with providing appropriate quality. This paper deals with considering peculiarities of using JPEG2000-based coders for compressing hyperspectral data.
Zemliachenko, Alexander +2 more
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Efficient Context-Aware Lossy Image Compression
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020We present an efficient context-aware lossy image compression system to participate in the Low Rate track of the CLIC 2020 Image Compression challenge. Our method is based on an autoencoder pipeline augmented with a nested hyperprior model, a PixelCNN-based context model and an adversarial loss to remove artefacts.
Jan Xu +5 more
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An automatic approach to lossy compression of AVIRIS images
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007Lossy compression of AVIRIS hyperspectral images is considered. An automatic approach to selection of compression parameters depending on noise characteristics in component images is proposed. Several ways of performing lossy compression are discussed and compared.
Nikolay N. Ponomarenko +4 more
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Image Compression Using VQ for Lossy Compression
2018The process to minimize the total number of bits required to depict an image is known as image compression. The main goal of image compression is to minimize the transmission cost and to reduce the storage space. Vector quantization is a most popular technique for lossy compression due to its high compression rate and simple decoding algorithm. The key
Rishav Chatterjee +2 more
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Lossy dictionary-based image compression method
Image and Vision Computing, 2007In this paper, we report on the new method of image compression. The method is based on LZ77 dictionary algorithm. We introduce two modifications such as quantization and noise levels. Experimental results presented in this paper prove that the new method of image compression gives promising results as compared with original LZ77 dictionary algorithm ...
Gabriela Dudek +2 more
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