4,472 research outputs found
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
Recurrent Scene Parsing with Perspective Understanding in the Loop
Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inversely proportional to the depth) so that small details are
preserved for distant objects while larger receptive fields are used for those
nearby. The depth gating signal is provided by stereo disparity or estimated
directly from monocular input. We integrate this depth-aware gating into a
recurrent convolutional neural network to perform semantic segmentation. Our
recurrent module iteratively refines the segmentation results, leveraging the
depth and semantic predictions from the previous iterations.
Through extensive experiments on four popular large-scale RGB-D datasets, we
demonstrate this approach achieves competitive semantic segmentation
performance with a model which is substantially more compact. We carry out
extensive analysis of this architecture including variants that operate on
monocular RGB but use depth as side-information during training, unsupervised
gating as a generic attentional mechanism, and multi-resolution gating. We find
that gated pooling for joint semantic segmentation and depth yields
state-of-the-art results for quantitative monocular depth estimation
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
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