9,318 research outputs found
Perceptual Image Similarity Metrics and Applications.
This dissertation presents research in perceptual image similarity metrics and applications, e.g., content-based image retrieval, perceptual image compression, image similarity assessment and texture analysis.
The first part aims to design texture similarity metrics consistent with human perception. A new family of statistical texture similarity features, called Local Radius Index (LRI), and corresponding similarity metrics are proposed. Compared to state-of-the-art metrics in the STSIM family, LRI-based metrics achieve better texture retrieval performance with much less computation. When applied to the recently developed perceptual image coder, Matched Texture Coding (MTC), they enable similar performance while significantly accelerating encoding. Additionally, in photographic paper classification, LRI-based metrics also outperform pre-existing metrics. To fulfill the needs of texture classification and other applications, a rotation-invariant version of LRI, called Rotation-Invariant Local Radius Index (RI-LRI), is proposed. RI-LRI is also grayscale and illuminance insensitive. The corresponding similarity metric achieves texture classification accuracy comparable to state-of-the-art metrics. Moreover, its much lower dimensional feature vector requires substantially less computation and storage than other state-of-the-art texture features.
The second part of the dissertation focuses on bilevel images, which are images whose pixels are either black or white. The contributions include new objective similarity metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of objective metrics. Several similarity metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlations with the ground truth. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram, Connected Components Comparison and combinations of such.
Another portion of the dissertation focuses on the aforementioned MTC, which is a block-based image coder that uses texture similarity metrics to decide if blocks of the image can be encoded by pointing to perceptually similar ones in the already coded region. The key to its success is an effective texture similarity metric, such as an LRI-based metric, and an effective search strategy. Compared to traditional image compression algorithms, e.g., JPEG, MTC achieves similar coding rate with higher reconstruction quality. And the advantage of MTC becomes larger as coding rate decreases.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113586/1/yhzhai_1.pd
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
Depth map compression via 3D region-based representation
In 3D video, view synthesis is used to create new virtual views between
encoded camera views. Errors in the coding of the depth maps introduce
geometry inconsistencies in synthesized views. In this paper, a new 3D plane
representation of the scene is presented which improves the performance of
current standard video codecs in the view synthesis domain. Two image segmentation
algorithms are proposed for generating a color and depth segmentation.
Using both partitions, depth maps are segmented into regions without
sharp discontinuities without having to explicitly signal all depth edges. The
resulting regions are represented using a planar model in the 3D world scene.
This 3D representation allows an efficient encoding while preserving the 3D
characteristics of the scene. The 3D planes open up the possibility to code
multiview images with a unique representation.Postprint (author's final draft
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