Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Pedro Garcia Freitas, Welington Y. L. Akamine, Mylène C. Q. Farias

ABSTRACT

In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods. More... »

PAGES

9

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13173-018-0073-3

DOI

http://dx.doi.org/10.1186/s13173-018-0073-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106023988


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HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

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N-Triples is a line-based linked data format ideal for batch operations.

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Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13173-018-0073-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13173-018-0073-3'


 

This table displays all metadata directly associated to this object as RDF triples.

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