With the rapid advancements in digital imaging systems and networking, low-cost hand-held
image capture devices equipped with network connectivity are becoming ubiquitous. This …

Providing effective support for intelligent vision tasks without image reconstruction can save
numerous computational costs in the era of big data. With the help of the Deep Neural …

The image compression-decompression process causes image quality degradations such
as blocking and ringing artifacts. A convolutional neural network based on DCT domain is …

The amount of multimedia data, such as images and videos, has been increasing rapidly
with the development of various imaging devices and the Internet, bringing more stress and …

The popularity of Convolutional Neural Network (CNN) in the field of Image Processing and
Computer Vision has motivated researchers and industry experts across the globe to solve …

An increasing amount of captured images are streamed to a remote server or stored in a
device for deep neural network (DNN) inference. In most cases, raw images are compressed …

We propose a highly accurate approach to remove artifacts of JPEG-compressed images.
Our approach jointly learns a very deep convolutional network in both DCT and pixel …

With the development of big data and network technology, there are more use cases, such
as edge computing, that require more secure and efficient multimedia big data transmission …

With the increasing tendency of the tempering of JPEG images, development of methods
detecting image forgery is of great importance. In many cases, JPEG image forgery is …

JPEG is one of the widely used lossy compression methods. JPEG-compressed images
usually suffer from compression artifacts including blocking and blurring, especially at low bit …