24 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
Preserving low-quality video through deep learning
Lossy video stream compression is performed to reduce the bandwidth and storage requirements. Moreover also image compression is a need that arises in many circumstances.It is often the case that older archive are stored at low resolution and with a compression rate suitable for the technology available at the time the video was created. Unfortunately, lossy compression algorithms cause artifact. Such artifacts, usually damage higher frequency details also adding noise or novel image patterns. There are several issues with this phenomenon. Low-quality images can be less pleasant to persons. Object detectors algorithms may have their performance reduced. As a result, given a perturbed version of it, we aim at removing such artifacts to recover the original image. To obtain that, one should reverse the compression process through a complicated non-linear image transformation. We propose a deep neural network able to improve image quality. We show that this model can be optimized either traditionally, directly optimizing an image similarity loss (SSIM), or using a generative adversarial approach (GAN). Our restored images have more photorealistic details with respect to traditional image enhancement networks. Our training procedure based on sub-patches is novel. Moreover, we propose novel testing protocol to evaluate restored images quantitatively. Differently from previously proposed approaches we are able to remove artifacts generated at any quality by inferring the image quality directly from data. Human evaluation and quantitative experiments in object detection show that our GAN generates images with finer consistent details and these details make a difference both for machines and humans