14,890 research outputs found
No-reference Image Denoising Quality Assessment
A wide variety of image denoising methods are available now. However, the
performance of a denoising algorithm often depends on individual input noisy
images as well as its parameter setting. In this paper, we present a
no-reference image denoising quality assessment method that can be used to
select for an input noisy image the right denoising algorithm with the optimal
parameter setting. This is a challenging task as no ground truth is available.
This paper presents a data-driven approach to learn to predict image denoising
quality. Our method is based on the observation that while individual existing
quality metrics and denoising models alone cannot robustly rank denoising
results, they often complement each other. We accordingly design denoising
quality features based on these existing metrics and models and then use Random
Forests Regression to aggregate them into a more powerful unified metric. Our
experiments on images with various types and levels of noise show that our
no-reference denoising quality assessment method significantly outperforms the
state-of-the-art quality metrics. This paper also provides a method that
leverages our quality assessment method to automatically tune the parameter
settings of a denoising algorithm for an input noisy image to produce an
optimal denoising result.Comment: 17 pages, 41 figures, accepted by Computer Vision Conference (CVC)
201
Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration
Learning based image quality assessment (IQA) models have obtained impressive
performance with the help of reliable subjective quality labels, where mean
opinion score (MOS) is the most popular choice. However, in view of the
subjective bias of individual annotators, the labor-abundant MOS (LA-MOS)
typically requires a large collection of opinion scores from multiple
annotators for each image, which significantly increases the learning cost. In
this paper, we aim to learn robust IQA models from low-cost MOS (LC-MOS), which
only requires very few opinion scores or even a single opinion score for each
image. More specifically, we consider the LC-MOS as the noisy observation of
LA-MOS and enforce the IQA model learned from LC-MOS to approach the unbiased
estimation of LA-MOS. In this way, we represent the subjective bias between
LC-MOS and LA-MOS, and the model bias between IQA predictions learned from
LC-MOS and LA-MOS (i.e., dual-bias) as two latent variables with unknown
parameters. By means of the expectation-maximization based alternating
optimization, we can jointly estimate the parameters of the dual-bias, which
suppresses the misleading of LC-MOS via a gated dual-bias calibration (GDBC)
module. To the best of our knowledge, this is the first exploration of robust
IQA model learning from noisy low-cost labels. Theoretical analysis and
extensive experiments on four popular IQA datasets show that the proposed
method is robust toward different bias rates and annotation numbers and
significantly outperforms the other learning based IQA models when only LC-MOS
is available. Furthermore, we also achieve comparable performance with respect
to the other models learned with LA-MOS
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