Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study

Ponomarenko, Nikolay N.; Krivenko, Sergey; Lukin, Vladimir; Егиазарян, Карен; Astola, Jaakko

doi:10.1155/2010/976436

Cited by 44 publications

(27 citation statements)

References 33 publications

(82 reference statements)

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“…Meanwhile, recent research results show that "old" standard images as, e.g., Lena, are, in fact, not noise-free [44,45]. This causes problems in correct estimation of filtering efficiency and careful comparison of filter performance.…”

Section: Introductionmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

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Section: Introductionmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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“…Images compressed with compression ratios below this threshold are then assumed diagnostically lossless. Interestingly, while performing these experiments, some researchers [6][7][8] noticed that when radiologists could perceive differences between both images they often preferred the compressed version. A possible explanation for this is that when compression ratios are increased beyond visually lossless thresholds, acquisition noise is significantly attenuated before the signal itself.…”

Section: Previous Workmentioning

confidence: 99%

Computed Tomography Image Compressibility and Limitations of Compression Ratio-Based Guidelines

Pambrun

Noumeir

2015

J Digit Imaging

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Finding optimal compression levels for diagnostic imaging is not an easy task. Significant compressibility variations exist between modalities, but little is known about compressibility variations within modalities. Moreover, compressibility is affected by acquisition parameters. In this study, we evaluate the compressibility of thousands of computed tomography (CT) slices acquired with different slice thicknesses, exposures, reconstruction filters, slice collimations, and pitches. We demonstrate that exposure, slice thickness, and reconstruction filters have a significant impact on image compressibility due to an increased high frequency content and a lower acquisition signal-to-noise ratio. We also show that compression ratio is not a good fidelity measure. Therefore, guidelines based on compression ratio should ideally be replaced with other compression measures better correlated with image fidelity. Value-of-interest (VOI) transformations also affect the perception of quality. We have studied the effect of value-of-interest transformation and found significant masking of artifacts when window is widened.

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“…11 It can even lead to a better visual quality of compressed images compared to original (noisy) ones if the parameters of a coder are properly adjusted and noise level in the original images is quite high. 12 A question, then, is what is "properly adjusted" with respect to properties of hyperspectral RS data and noise present in the corresponding images.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the OOP position depends upon a metric and the coder used. 12 Below we obtain and analyze data for the coder AGU 47 that performs image compression in 32 × 32 pixel blocks and deblocks after decompression. There are several reasons for using this coder in our further analysis.…”

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confidence: 99%

“…47 Second, QS serves as PCC for this coder, and it is easy to control compression (to vary CR) by changing QS. 12,47 Third, there is also a 3-D version of AGU that has earlier been successfully applied for compressing AVIRIS hyperspectral data with sub-band grouping. 13,40 Therefore, let us come to demonstrating and analyzing typical dependences presented in Fig.…”

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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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Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study

Cited by 44 publications

References 33 publications

Efficiency analysis of color image filtering

Efficiency analysis of color image filtering

Computed Tomography Image Compressibility and Limitations of Compression Ratio-Based Guidelines

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

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