9,177 research outputs found
Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process
In this paper, we propose a sparse representation based Reduced-Reference Image Quality Assessment (RR-IQA) index for stereoscopic images from the following two perspectives: 1) Human visual system (HVS) always tries to infer the meaningful information and reduces uncertainty from the visual stimuli, and the entropy of primitive (EoP) can well describe this visual cognitive progress when perceiving natural images. 2) Ocular dominance (also known as binocularity) which represents the interaction between two eyes is quantified by the sparse representation coefficients. Inspired by previous research, the perception and understanding of an image is considered as an active inference process determined by the level of “surprise”, which can be described by EoP. Therefore, the primitives learnt from natural images can be utilized to evaluate the visual information by computing entropy. Meanwhile, considering the binocularity in stereo image quality assessment, a feasible way is proposed to characterize this binocular process according to the sparse representation coefficients of each view. Experimental results on LIVE 3D image databases and MCL database further demonstrate that the proposed algorithm achieves high consistency with subjective evaluation
Unsupervised edge map scoring: a statistical complexity approach
We propose a new Statistical Complexity Measure (SCM) to qualify edge maps
without Ground Truth (GT) knowledge. The measure is the product of two indices,
an \emph{Equilibrium} index obtained by projecting the edge map
into a family of edge patterns, and an \emph{Entropy} index ,
defined as a function of the Kolmogorov Smirnov (KS) statistic.
This new measure can be used for performance characterization which includes:
(i)~the specific evaluation of an algorithm (intra-technique process) in order
to identify its best parameters, and (ii)~the comparison of different
algorithms (inter-technique process) in order to classify them according to
their quality.
Results made over images of the South Florida and Berkeley databases show
that our approach significantly improves over Pratt's Figure of Merit (PFoM)
which is the objective reference-based edge map evaluation standard, as it
takes into account more features in its evaluation
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image
Generative Adversarial Network-based architecture. The proposed model is
trained by under weak supervision: unlike previous works, there is no need for
strong supervision in the form of a large annotated dataset of aligned
original/enhanced photo pairs. The sole requirement is two distinct datasets:
one from the source camera, and one composed of arbitrary high-quality images
that can be generally crawled from the Internet - the visual content they
exhibit may be unrelated. Hence, our solution is repeatable for any camera:
collecting the data and training can be achieved in a couple of hours. In this
work, we emphasize on extensive evaluation of obtained results. Besides
standard objective metrics and subjective user study, we train a virtual rater
in the form of a separate CNN that mimics human raters on Flickr data and use
this network to get reference scores for both original and enhanced photos. Our
experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from
several generations of smartphones demonstrate that WESPE produces comparable
or improved qualitative results with state-of-the-art strongly supervised
methods
Application of remote sensing to state and regional problems
The methods and procedures used, accomplishments, current status, and future plans are discussed for each of the following applications of LANDSAT in Mississippi: (1) land use planning in Lowndes County; (2) strip mine inventory and reclamation; (3) white-tailed deer habitat evaluation; (4) remote sensing data analysis support systems; (5) discrimination of unique forest habitats in potential lignite areas; (6) changes in gravel operations; and (7) determining freshwater wetlands for inventory and monitoring. The documentation of all existing software and the integration of the image analysis and data base software into a single package are now considered very high priority items
Data compression for full motion video transmission
Clearly transmission of visual information will be a major, if not dominant, factor in determining the requirements for, and assessing the performance of the Space Exploration Initiative (SEI) communications systems. Projected image/video requirements which are currently anticipated for SEI mission scenarios are presented. Based on this information and projected link performance figures, the image/video data compression requirements which would allow link closure are identified. Finally several approaches which could satisfy some of the compression requirements are presented and possible future approaches which show promise for more substantial compression performance improvement are discussed
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