209 research outputs found
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features
Localization of just noticeable difference for image compression
The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given reference image and a compression algorithm represents the minimal level of compression that causes noticeable differences in the reconstruction. These differences can only be observed in some specific regions within the image, dubbed as JND-critical regions. Identifying these regions can improve the development of image compression algorithms. Due to the fact that visual perception varies among individuals, determining the PJND values and JND-critical regions for a target population of consumers requires subjective assessment experiments involving a sufficiently large number of observers. In this paper, we propose a novel framework for conducting such experiments using crowdsourcing. By applying this framework, we created a novel PJND dataset, KonJND++, consisting of 300 source images, compressed versions thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and 129 self-reported locations of JND-critical regions for each source image. Our experiments demonstrate the effectiveness and reliability of our proposed framework, which is easy to be adapted for collecting a large-scale dataset. The source code and dataset are available at https://github.com/angchen-dev/LocJND.</p
Localization of Just Noticeable Difference for Image Compression
The just noticeable difference (JND) is the minimal difference between
stimuli that can be detected by a person. The picture-wise just noticeable
difference (PJND) for a given reference image and a compression algorithm
represents the minimal level of compression that causes noticeable differences
in the reconstruction. These differences can only be observed in some specific
regions within the image, dubbed as JND-critical regions. Identifying these
regions can improve the development of image compression algorithms. Due to the
fact that visual perception varies among individuals, determining the PJND
values and JND-critical regions for a target population of consumers requires
subjective assessment experiments involving a sufficiently large number of
observers. In this paper, we propose a novel framework for conducting such
experiments using crowdsourcing. By applying this framework, we created a novel
PJND dataset, KonJND++, consisting of 300 source images, compressed versions
thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and
129 self-reported locations of JND-critical regions for each source image. Our
experiments demonstrate the effectiveness and reliability of our proposed
framework, which is easy to be adapted for collecting a large-scale dataset.
The source code and dataset are available at
https://github.com/angchen-dev/LocJND
The application of visual saliency models in objective image quality assessment: a statistical evaluation
Advances in image quality assessment have shown the potential added value of including visual attention aspects in its objective assessment. Numerous models of visual saliency are implemented and integrated in different image quality metrics (IQMs), but the gain in reliability of the resulting IQMs varies to a large extent. The causes and the trends of this variation would be highly beneficial for further improvement of IQMs, but are not fully understood. In this paper, an exhaustive statistical evaluation is conducted to justify the added value of computational saliency in objective image quality assessment, using 20 state-of-the-art saliency models and 12 best-known IQMs. Quantitative results show that the difference in predicting human fixations between saliency models is sufficient to yield a significant difference in performance gain when adding these saliency models to IQMs. However, surprisingly, the extent to which an IQM can profit from adding a saliency model does not appear to have direct relevance to how well this saliency model can predict human fixations. Our statistical analysis provides useful guidance for applying saliency models in IQMs, in terms of the effect of saliency model dependence, IQM dependence, and image distortion dependence. The testbed and software are made publicly available to the research community
Focus-and-Context Skeleton-Based Image Simplification Using Saliency Maps
Medial descriptors offer a promising way for representing, simplifying, manipulating, and compressing images. However, to date, these have been applied in a global manner that is oblivious to salient features. In this paper, we adapt medial descriptors to use the information provided by saliency maps to selectively simplify and encode an image while preserving its salient regions. This allows us to improve the trade-off between compression ratio and image quality as compared to the standard dense-skeleton method while keeping perceptually salient features, in a focus-and-context manner. We show how our method can be combined with JPEG to increase overall compression rates at the cost of a slightly lower image quality. We demonstrate our method on a benchmark composed of a broad set of images
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