4 research outputs found
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
In this paper, we introduce an adaptive unsupervised learning framework,
which utilizes natural images to train filter sets. The applicability of these
filter sets is demonstrated by evaluating their performance in two contrasting
applications - image quality assessment and texture retrieval. While assessing
image quality, the filters need to capture perceptual differences based on
dissimilarities between a reference image and its distorted version. In texture
retrieval, the filters need to assess similarity between texture images to
retrieve closest matching textures. Based on experiments, we show that the
filter responses span a set in which a monotonicity-based metric can measure
both the perceptual dissimilarity of natural images and the similarity of
texture images. In addition, we corrupt the images in the test set and
demonstrate that the proposed method leads to robust and reliable retrieval
performance compared to existing methods.Comment: Paper:5 pages, 5 figures, 3 tables and Poster [Ancillary files
UNIQUE: Unsupervised Image Quality Estimation
In this paper, we estimate perceived image quality using sparse
representations obtained from generic image databases through an unsupervised
learning approach. A color space transformation, a mean subtraction, and a
whitening operation are used to enhance descriptiveness of images by reducing
spatial redundancy; a linear decoder is used to obtain sparse representations;
and a thresholding stage is used to formulate suppression mechanisms in a
visual system. A linear decoder is trained with 7 GB worth of data, which
corresponds to 100,000 8x8 image patches randomly obtained from nearly 1,000
images in the ImageNet 2013 database. A patch-wise training approach is
preferred to maintain local information. The proposed quality estimator UNIQUE
is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases
and compared with thirteen quality estimators. Experimental results show that
UNIQUE is generally a top performing quality estimator in terms of accuracy,
consistency, linearity, and monotonic behavior.Comment: 12 pages, 5 figures, 2 table
Understanding perceived quality through visual representations
The formatting of images can be considered as an optimization problem, whose cost function is a quality assessment algorithm. There is a trade-off between bit budget per pixel and quality. To maximize the quality and minimize the bit budget, we need to measure the perceived quality. In this thesis, we focus on understanding perceived quality through visual representations that are based on visual system characteristics and color perception mechanisms. Specifically, we use the contrast sensitivity mechanisms in retinal ganglion cells and the suppression mechanisms in cortical neurons. We utilize color difference equations and color name distances to mimic pixel-wise color perception and a bio-inspired model to formulate center surround effects. Based on these formulations, we introduce two novel image quality estimators PerSIM and CSV, and a new image quality-assistance method BLeSS. We combine our findings from visual system and color perception with data-driven methods to generate visual representations and measure their quality. The majority of existing data-driven methods require subjective scores or degraded images. In contrast, we follow an unsupervised approach that only utilizes generic images. We introduce a novel unsupervised image quality estimator UNIQUE, and extend it with multiple models and layers to obtain MS-UNIQUE and DMS-UNIQUE. In addition to introducing quality estimators, we analyze the role of spatial pooling and boosting in image quality assessment.Ph.D