No-reference objective wavelet based noise immune image sharpness metric

Ferzli, Rony; Karam, Lina J.

doi:10.1109/icip.2005.1529773

Cited by 55 publications

(34 citation statements)

References 7 publications

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“…At an edge, for example, the profile of a blurry high resolution image will be more gradual than its corresponding low resolution standard thumbnail, t s , whose profile will be steeper. 10 Applying successively larger blurs to t s will cause its edge profile to become more gradual, and to correspond better to the blurry original. To have the system work with various image features, and not just edges, the computation is based on pixel range (difference between maximum and minimum pixel values in a spatial neighborhood) to determine the local image profiles.…”

Section: Algorithm For Automatically Generated Representative Thumbnailsmentioning

confidence: 99%

Representative image thumbnails: automatic and manual

Samadani

Mauer²,

Berfanger³

et al. 2008

SPIE Proceedings

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Section: Algorithm For Automatically Generated Representative Thumbnailsmentioning

confidence: 99%

Representative image thumbnails: automatic and manual

Samadani

Mauer²,

Berfanger³

et al. 2008

SPIE Proceedings

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“…Later more and more research is based on the JNB [40], [41], [42], [43], [44]. The other way to predict a certain distortion is transform-based, such as Discrete cosine transform (DCT) [45] and Discrete wavelet transform (DWT) [46], [47]. Local Phase CoherenceSharpness Index (LPC-SI) proposed by Hassen et al [48], [49], which identifies sharpness as strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain.…”

Section: ) Full Reference Metricsmentioning

confidence: 99%

Evaluation of image quality metrics for sharpness enhancement

Cheng

Pedersen

Chen

2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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Abstract-Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.

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“…A no-reference noise-immune image sharpness metric was also proposed [5]. Furthermore, all the edge-based sharpness metrics can be easily applied in the wavelet domain as described in [5] to provide resilience to noise. Still, it lacks the ability to assess the impairment due to noise.…”

Section: Introductionmentioning

confidence: 99%

A no-reference objective image quality metric based on perceptually weighted local noise

Zhu

Karam

2014

J Image Video Proc

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This work proposes a perceptual based no-reference objective image quality metric by integrating perceptually weighted local noise into a probability summation model. Unlike existing objective metrics, the proposed no-reference metric is able to predict the relative amount of noise perceived in images with different content, without a reference. Results are reported on both the LIVE and TID2008 databases. The proposed no-reference metric achieves consistently a good performance across noise types and across databases as compared to many of the best very recent no-reference quality metrics. The proposed metric is able to predict with high accuracy the relative amount of perceived noise in images of different content.

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No-reference objective wavelet based noise immune image sharpness metric

Cited by 55 publications

References 7 publications

Representative image thumbnails: automatic and manual

Representative image thumbnails: automatic and manual

Evaluation of image quality metrics for sharpness enhancement

A no-reference objective image quality metric based on perceptually weighted local noise

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