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Method and apparatus for processing both still and moving visual pattern images
An improved method for coding and decoding still or moving visual pattern images by partitioning images into blocks or cubes, respectively, and coding each image separately according to visually significant responses of the human eye. Coding is achieved by calculating and subtracting a mean intensity value from digital numbers within each block or cube and detecting visually perceivable edge locations within the resultant residual sub-image. If a visually perceivable edge is contained within the block or cube, gradient magnitude and orientation at opposing sides of the edge within each edge block or cube are calculated and appropriately coded. If no perceivable edge is contained within the block or cube, the sub-image is coded as a uniform intensity block. Decoding requires receiving coded mean intensity value, gradient magnitude and pattern code, and then decoding a combination of these three indicia to be arranged in an orientation substantially similar to the original digital image or original sequence of digital images. Coding and decoding can be accomplished in a hierarchical pattern. Further, hierarchical processing can be programmably manipulated according to user-defined criteria.Board of Regents, University of Texas Syste
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
Automatic Perceptual Image Quality Assessment is a challenging problem that
impacts billions of internet, and social media users daily. To advance research
in this field, we propose a Mixture of Experts approach to train two separate
encoders to learn high-level content and low-level image quality features in an
unsupervised setting. The unique novelty of our approach is its ability to
generate low-level representations of image quality that are complementary to
high-level features representing image content. We refer to the framework used
to train the two encoders as Re-IQA. For Image Quality Assessment in the Wild,
we deploy the complementary low and high-level image representations obtained
from the Re-IQA framework to train a linear regression model, which is used to
map the image representations to the ground truth quality scores, refer Figure
1. Our method achieves state-of-the-art performance on multiple large-scale
image quality assessment databases containing both real and synthetic
distortions, demonstrating how deep neural networks can be trained in an
unsupervised setting to produce perceptually relevant representations. We
conclude from our experiments that the low and high-level features obtained are
indeed complementary and positively impact the performance of the linear
regressor. A public release of all the codes associated with this work will be
made available on GitHub.Comment: Accepted to IEEE/CVF CVPR 2023. Code will be released post conference
in July 2023. Avinab Saha & Sandeep Mishra contributed equally to this wor
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