62 research outputs found
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
address high-level computer vision tasks very successfully. Recently, they have
found their way into the areas of low-level computer vision and image
processing to solve regression problems mostly with relatively shallow
networks.
We present a novel 12-layer deep convolutional network for image compression
artifact suppression with hierarchical skip connections and a multi-scale loss
function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an
improvement of up to 0.36 dB over the best previous ConvNet result. We show
that a network trained for a specific quality factor (QF) is resilient to the
QF used to compress the input image - a single network trained for QF 60
provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page
LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space
Light field imaging can capture both the intensity information and the
direction information of light rays. It naturally enables a
six-degrees-of-freedom viewing experience and deep user engagement in virtual
reality. Compared to 2D image assessment, light field image quality assessment
(LFIQA) needs to consider not only the image quality in the spatial domain but
also the quality consistency in the angular domain. However, there is a lack of
metrics to effectively reflect the angular consistency and thus the angular
quality of a light field image (LFI). Furthermore, the existing LFIQA metrics
suffer from high computational costs due to the excessive data volume of LFIs.
In this paper, we propose a novel concept of "anglewise attention" by
introducing a multihead self-attention mechanism to the angular domain of an
LFI. This mechanism better reflects the LFI quality. In particular, we propose
three new attention kernels, including anglewise self-attention, anglewise grid
attention, and anglewise central attention. These attention kernels can realize
angular self-attention, extract multiangled features globally or selectively,
and reduce the computational cost of feature extraction. By effectively
incorporating the proposed kernels, we further propose our light field
attentional convolutional neural network (LFACon) as an LFIQA metric. Our
experimental results show that the proposed LFACon metric significantly
outperforms the state-of-the-art LFIQA metrics. For the majority of distortion
types, LFACon attains the best performance with lower complexity and less
computational time.Comment: Accepted for IEEE VR 2023 (TVCG Special Issues) (Early Access
Removal Of Blocking Artifacts From JPEG-Compressed Images Using An Adaptive Filtering Algorithm
The aim of this research was to develop an algorithm that will produce a considerable improvement in the quality of JPEG images, by removing blocking and ringing artifacts, irrespective of the level of compression present in the image. We review multiple published related works, and finally present a computationally efficient algorithm for reducing the blocky and Gibbs oscillation artifacts commonly present in JPEG compressed images. The algorithm alpha-blends a smoothed version of the image with the original image; however, the blending is controlled by a limit factor that considers the amount of compression present and any local edge information derived from the application of a Prewitt filter. In addition, the actual value of the blending coefficient (α) is derived from the local Mean Structural Similarity Index Measure (MSSIM) which is also adjusted by a factor that also considers the amount of compression present. We also present our results as well as the results for a variety of other papers whose authors used other post compression filtering methods
A Design Method of Saturation Test Image Based on CIEDE2000
In order to generate color test image consistent with human perception in aspect of saturation, lightness, and hue of image, we propose a saturation test image design method based on CIEDE2000 color difference formula. This method exploits the subjective saturation parameter C′ of CIEDE2000 to get a series of test images with different saturation but same lightness and hue. It is found experimentally that the vision perception has linear relationship with the saturation parameter C′. This kind of saturation test image has various applications, such as in the checking of color masking effect in visual experiments and the testing of the visual effects of image similarity component
DriftRec: Adapting diffusion models to blind JPEG restoration
In this work, we utilize the high-fidelity generation abilities of diffusion
models to solve blind JPEG restoration at high compression levels. We propose
an elegant modification of the forward stochastic differential equation of
diffusion models to adapt them to this restoration task and name our method
DriftRec. Comparing DriftRec against an regression baseline with the same
network architecture and two state-of-the-art techniques for JPEG restoration,
we show that our approach can escape the tendency of other methods to generate
blurry images, and recovers the distribution of clean images significantly more
faithfully. For this, only a dataset of clean/corrupted image pairs and no
knowledge about the corruption operation is required, enabling wider
applicability to other restoration tasks. In contrast to other conditional and
unconditional diffusion models, we utilize the idea that the distributions of
clean and corrupted images are much closer to each other than each is to the
usual Gaussian prior of the reverse process in diffusion models. Our approach
therefore requires only low levels of added noise, and needs comparatively few
sampling steps even without further optimizations. We show that DriftRec
naturally generalizes to realistic and difficult scenarios such as unaligned
double JPEG compression and blind restoration of JPEGs found online, without
having encountered such examples during training.Comment: This work has been submitted to the IEEE for possible publication.
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Stereoscopic video quality assessment based on 3D convolutional neural networks
The research of stereoscopic video quality assessment (SVQA) plays an important role for promoting the development of stereoscopic video system. Existing SVQA metrics rely on hand-crafted features, which is inaccurate and time-consuming because of the diversity and complexity of stereoscopic video distortion. This paper introduces a 3D convolutional neural networks (CNN) based SVQA framework that can model not only local spatio-temporal information but also global temporal information with cubic difference video patches as input. First, instead of using hand-crafted features, we design a 3D CNN architecture to automatically and effectively capture local spatio-temporal features. Then we employ a quality score fusion strategy considering global temporal clues to obtain final video-level predicted score. Extensive experiments conducted on two public stereoscopic video quality datasets show that the proposed method correlates highly with human perception and outperforms state-of-the-art methods by a large margin. We also show that our 3D CNN features have more desirable property for SVQA than hand-crafted features in previous methods, and our 3D CNN features together with support vector regression (SVR) can further boost the performance. In addition, with no complex preprocessing and GPU acceleration, our proposed method is demonstrated computationally efficient and easy to use
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