38 research outputs found
A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
The process of quantifying image quality consists of engineering the quality
features and pooling these features to obtain a value or a map. There has been
a significant research interest in designing the quality features but pooling
is usually overlooked compared to feature design. In this work, we compare the
state of the art quality and content-based spatial pooling strategies and show
that although features are the key in any image quality assessment, pooling
also matters. We also propose a quality-based spatial pooling strategy that is
based on linearly weighted percentile pooling (WPP). Pooling strategies are
analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE
and TID2013 image databases.Comment: Paper: 5 pages, 8 figures, Presentation: 21 slides [Ancillary files
No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image
Light field image quality assessment (LF-IQA) plays a significant role due to
its guidance to Light Field (LF) contents acquisition, processing and
application. The LF can be represented as 4-D signal, and its quality depends
on both angular consistency and spatial quality. However, few existing LF-IQA
methods concentrate on effects caused by angular inconsistency. Especially,
no-reference methods lack effective utilization of 2-D angular information. In
this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The
Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can
simultaneously record the angular information in both horizontal and vertical
directions. Since the MLI contains 2-D angular information, we propose a
No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI).
Specifically, we first utilize Global Entropy Distribution (GED) and Uniform
Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and
then pool them together to measure angular consistency. In addition, the
information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial
quality. Extensive experimental results show that LF-QMLI achieves the
state-of-the-art performance