Lossy Compression of Images with Additive Noise

Ponomarenko, Nikolay N.; Zriakhov, Mikhail; Егиазарян, Карен; Astola, Jaakko

doi:10.1007/11558484_48

Cited by 37 publications

(43 citation statements)

References 11 publications

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“…First, this means that the coder introduces the losses that mainly relate to noise filtering. Second, similar effects were earlier observed for lossy compression of images corrupted by additive noise: 48 MSE nc in the neighborhood of OOP and additive noise variance were approximately equal. Moreover, this property was put into the basis of automatic iterative procedure of OOP attained in Ref.…”

Section: Analysis Of Component-wise Compression Efficiency For Real-lsupporting

confidence: 56%

“…This was one more reason for considering the AGU coder that uses QS as PCC and that has been thoroughly studied in the papers. 12,50 It has also been shown in the papers 34,36,50 that earlier proposed automatic procedures 12,48,49 for OOP attaining can also be applied for compressing images corrupted by different types of SD noises. The main idea is in applying a proper homomorphic (variance stabilizing) image transformation before compression with converting SD noise to pure additive (or almost pure additive 34 ) in transformed images.…”

mentioning

confidence: 99%

“…Several procedures have been already proposed and tested for a case of additive noise. 12,48,49 The procedure in Ref. 48 is iterative and, under conditions of the known variance of additive noise, it can be used for any lossy compression method.…”

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confidence: 99%

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Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Zemliachenko

Kozhemiakin

Uss

et al. 2014

J. Appl. Remote Sens

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Abstract. A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

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Section: Analysis Of Component-wise Compression Efficiency For Real-lsupporting

confidence: 56%

mentioning

confidence: 99%

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Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Zemliachenko

Kozhemiakin

Uss

et al. 2014

J. Appl. Remote Sens

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“…The author of [2] have discussed compression technique on DCT based picture compression in which PSNR is used to measure the performance of generated pictures over JPEG and JPEG2000. In designing mathematical model [3] for the noise in binary coding of multiplexed signals in imaging system signal to noise ratios are used in deciding performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is subsequently increased in developing the interest of picture and video administrations. Be that as it may, advanced picture handling experience the ill effects of inherent noise from their phase of capturing [1], compressing [2], encoding [3], communication [4], decoding and processing stages [5]. This noise appearance will change the normal arrangement of original data in DP/VF signal.…”

Section: Introductionmentioning

confidence: 99%

Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures

Suresha¹,

Prakash²

2017

IJIGSP

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Abstract-Estimating the visual quality of picture is a real challenge for various picture and video frame applications. The aim is to evaluate the quality of picture automatically in both subjective (human visual frame work) and objectively. The quality of picture is evaluated by comparing precision and closeness of a picture with reference or error free picture. The quality estimation can be done to achieve consistency in desired quality of picture with help of modeling remarkable physiological, psycho visual components framework and picture fidelity measure methods. In this article, the picture quality is evaluated by analyzing loss of picture information of the distortion system using differing noise models and examine the relationship between picture data, visual quality and error metric. The quality of picture & video frame assessment is really important that, every human can judge the visual quality of natural picture. The subjective quality of picture is assessed by using structural similarity metric, objective quality of picture is computed by root means squared error, mean squared error and peak signal to noise ratio and data content in picture is weighted through entropy.

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Approaches to Classification of Multichannel Images

Ponomarenko

Kurekin

Lever

et al. 2006

Lecture Notes in Computer Science

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The comparison of different approaches to classification of multichannel remote sensing images obtained by spaceborne imaging systems is presented. It is demonstrated that it is reasonable to compress original noisy images with appropriate compression ratio and then to classify the decompressed images rather than original data. Two classifiers are considered: based on radial basis function neural network and support vector machine. The latter one produces slightly better classification results.

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Lossy Compression of Images with Additive Noise

Cited by 37 publications

References 11 publications

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures

Approaches to Classification of Multichannel Images

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