Document image quality assessment based on improved gradient magnitude similarity deviation

Alaei, Alireza; Conte, Donatello; Raveaux, Romain

doi:10.1109/icdar.2015.7333747

Cited by 16 publications

(35 citation statements)

References 12 publications

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“…For each foreground patch, the corresponding gradient maps obtained from the reference and distorted gradient magnitude maps have been used to compute a gradient magnitude similarity map of the patch. Gradient magnitude similarity deviation of the patch has then been calculated and an average pooling has finally been performed to obtain the final image quality metric of the distorted document image [14]. In [14], we proved that the MGMSD metric was more effective on document images than some existing measures.…”

Section: Introductionmentioning

confidence: 85%

“…A few research studies have also been carried out in the domain of document image analysis to develop metrics, which compute automatic objective image quality for document images [11][12][13][14]. In these methods, HOSs have been used as ground truth to evaluate how objective image quality computed by a machine is close to the human perception-based image quality scores provided by individuals [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In these methods, HOSs have been used as ground truth to evaluate how objective image quality computed by a machine is close to the human perception-based image quality scores provided by individuals [11][12][13][14]. A FR DIQA method has been presented for binary document images at the character level in [11].…”

Section: Introductionmentioning

confidence: 99%

“…The difference of changes in gray scale values of a median-filtered image has been used as an indication of edge sharpness [13]. Recently, a FR DIQA method based on gradient magnitude features has further been proposed [14]. The method (MGMSD) presented in [14] has mainly used foreground information as a set of patches extracted from the reference document images employing a segmentation technique.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a FR DIQA method based on gradient magnitude features has further been proposed [14]. The method (MGMSD) presented in [14] has mainly used foreground information as a set of patches extracted from the reference document images employing a segmentation technique. For each foreground patch, the corresponding gradient maps obtained from the reference and distorted gradient magnitude maps have been used to compute a gradient magnitude similarity map of the patch.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Document Image Quality Assessment Based on Texture Similarity Index

Alaei¹,

Conte²,

Blumenstein³

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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International audienceIn this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image-based dataset

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Document Image Quality Assessment Based on Texture Similarity Index

Alaei¹,

Conte²,

Blumenstein³

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

Self Cite

View full text Add to dashboard Cite

show abstract

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

Walha

Drira

Lebourgeois

et al. 2018

IJDAR

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Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

Alaei

Conte

Martineau

et al. 2018

Expert Systems with Applications

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The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-today life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is rst sampled into a set of patches. The extracted patches are then ltered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is nally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the eectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.

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Document image quality assessment based on improved gradient magnitude similarity deviation

Cited by 16 publications

References 12 publications

Document Image Quality Assessment Based on Texture Similarity Index

Document Image Quality Assessment Based on Texture Similarity Index

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

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