53,644 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
Scale-wise Convolution for Image Restoration
While scale-invariant modeling has substantially boosted the performance of
visual recognition tasks, it remains largely under-explored in deep networks
based image restoration. Naively applying those scale-invariant techniques
(e.g. multi-scale testing, random-scale data augmentation) to image restoration
tasks usually leads to inferior performance. In this paper, we show that
properly modeling scale-invariance into neural networks can bring significant
benefits to image restoration performance. Inspired from spatial-wise
convolution for shift-invariance, "scale-wise convolution" is proposed to
convolve across multiple scales for scale-invariance. In our scale-wise
convolutional network (SCN), we first map the input image to the feature space
and then build a feature pyramid representation via bi-linear down-scaling
progressively. The feature pyramid is then passed to a residual network with
scale-wise convolutions. The proposed scale-wise convolution learns to
dynamically activate and aggregate features from different input scales in each
residual building block, in order to exploit contextual information on multiple
scales. In experiments, we compare the restoration accuracy and parameter
efficiency among our model and many different variants of multi-scale neural
networks. The proposed network with scale-wise convolution achieves superior
performance in multiple image restoration tasks including image
super-resolution, image denoising and image compression artifacts removal. Code
and models are available at: https://github.com/ychfan/scn_srComment: AAAI 202
Deep Multiple Description Coding by Learning Scalar Quantization
In this paper, we propose a deep multiple description coding framework, whose
quantizers are adaptively learned via the minimization of multiple description
compressive loss. Firstly, our framework is built upon auto-encoder networks,
which have multiple description multi-scale dilated encoder network and
multiple description decoder networks. Secondly, two entropy estimation
networks are learned to estimate the informative amounts of the quantized
tensors, which can further supervise the learning of multiple description
encoder network to represent the input image delicately. Thirdly, a pair of
scalar quantizers accompanied by two importance-indicator maps is automatically
learned in an end-to-end self-supervised way. Finally, multiple description
structural dissimilarity distance loss is imposed on multiple description
decoded images in pixel domain for diversified multiple description generations
rather than on feature tensors in feature domain, in addition to multiple
description reconstruction loss. Through testing on two commonly used datasets,
it is verified that our method is beyond several state-of-the-art multiple
description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing
datasets for "Deep Optimized Multiple Description Image Coding via Scalar
Quantization Learning" can be found in the website of
https://github.com/mdcnn/Deep-Multiple-Description-Codin
Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Networked video applications, e.g., video conferencing, often suffer from
poor visual quality due to unexpected network fluctuation and limited
bandwidth. In this paper, we have developed a Quality Enhancement Network
(QENet) to reduce the video compression artifacts, leveraging the spatial and
temporal priors generated by respective multi-scale convolutions spatially and
warped temporal predictions in a recurrent fashion temporally. We have
integrated this QENet as a standard-alone post-processing subsystem to the High
Efficiency Video Coding (HEVC) compliant decoder. Experimental results show
that our QENet demonstrates the state-of-the-art performance against default
in-loop filters in HEVC and other deep learning based methods with noticeable
objective gains in Peak-Signal-to-Noise Ratio (PSNR) and subjective gains
visually
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