Blur determination in the compressed domain using DCT information

Marichal, Xavier; Ma, Wei‐Ying; Zhang, Hong-Jiang

doi:10.1109/icip.1999.822923

Cited by 162 publications

(87 citation statements)

References 5 publications

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“…The required degree of difference for key-frame selection can be adjusted in the algorithm to control the approximate number of key-frames generated. Note that to be selected as a key-frame, each key-frame must satisfy a certain requirement to ensure it is not blurred [13]. Key-frames from each shot are clustered to form a hierarchical representation that provides multiple levels of granulations for video browsing and navigation [30].…”

Section: Shot Boundary Detection and Key-frame Selectionmentioning

confidence: 99%

An interactive video delivery and caching system using video summarization

Lee

Shen

2002

Computer Communications

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Section: Shot Boundary Detection and Key-frame Selectionmentioning

confidence: 99%

An interactive video delivery and caching system using video summarization

Lee

Shen

2002

Computer Communications

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“…Afterwards, the blocks gradients and magnitude directions are calculated. X. Marichal et al [16] studied a global blur metric through the high frequency coefficients of the Discrete Cosine Transform. A blurred image is the one whose is the high frequency coefficients are close to 0.…”

Section: A Image Blur Measurementmentioning

confidence: 99%

QR Code Patterns Localization based on Hu Invariant Moments

Tribak¹,

Zaz²

2017

ijacsa

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Abstract-The widespread utilization of QR code and its coincidence with the swift growth of e-commerce transactions have imposed the computer vision researchers to continuously devise a variety of QR code recognition algorithms. The latter performances are generally limited due to two main factors. Firstly, most of them are computationally expensive because of the implemented feature descriptor complexities. Secondly, the evoked algorithms are often sensitive to pattern geometric deformations. In this paper a robust approach is proposed, in which the architecture is based on three distinct treatments among others: 1) An image quality assessment stage which evaluates the quality of the captured image in consideration that the presence of blur decreases significantly the recognition accuracy. 2) This stage is followed by an image segmentation based on an achromatic filter through which only the regions of interest are highlighted and consequently the execution time is reduced. 3) Finally, the Hu invariant moments technique is used as feature descriptor permitting removing false positives. This technique is implemented to filter out the set of extracted candidate QR code patterns, which have been roughly extracted by a scanning process. The Hu moments descriptor is able to recognize patterns independently of the geometric transformations they undergo. The experiments show that the incorporation of the aforementioned three stages enhances significantly the recognition accuracy along with a notable diminution of processing time. This makes the proposed approach adapted to embedded systems and devices with limited performances.

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“…The blur metric in [33] is based on histogram computations of DCT coefficients and can therefore instantly be applied in the compressed domain of JPEG images or MPEG frames. The idea behind this is to take advantage of image analysis which has already been performed in the compression process.…”

Section: A Single Feature Metricsmentioning

confidence: 99%