Image Compression Standard
Image compression is the process of encoding digital image data with fewer bits than an unencoded representation by employing specific encoding schemes. When storing and transmitting images, image compression is a popular method to reduce storage and bandwidth requirements. However, as a result of image compression, the image quality suffers. We offer solutions to restore image quality using techniques such as Super Resolution.
We live in an unprecedented period of time where we are surrounded by numerous digital devices that enable us to carry out our daily activities—such as working, playing, and caring for others—much more effectively than in the past. Many of these activities include streaming vast amounts of data made up of photos and videos, and the leisure industry is always raising the standard of experience to provide a better user experience, such as HD vs. 4K UHD vs. 8K UHD, SDR vs. HDR.
International standards organizations invest a lot of time creating new encoding standards to address these needs, but are still unable to meet all the demands made by the market and the growing number of applications. As a consequence, video quality is constantly being increased at the expense of compression efficiency.
Therefore, a fair balance between quality and compression efficiency must be found amongst a number of factors including bandwidth, service delivery costs, etc. On the other hand, deep-learning techniques are proposed either as end-to-end approaches to provide a significant improvement of the overall quality of the compression standard itself, or to substitute blocks (i.e., intra-coding, downsampling/upsampling blocks) into an existing compression.
AI-Based Media Coding
As a founding member of the MPAI standard, our group is taking part in the exploration phase to see whether it is possible to improve the performance of the Essential Video Coding (EVC) by enhancing or replacing existing video coding tools with AI tools while keeping complexity increase to an acceptable level.
To achieve our goals of enhancing traditional video coding tools with AI, our team is currently working on providing a solution for the Super Resolution (SR) Problem that is comparable to state-of-the-art models with less memory consumption and better time efficiency.
Super Resolution is the process of recovering a High Resolution (HR) image from a given Low Resolution (LR) image. It is an important class of image processing techniques in computer vision and image processing and enjoys a wide range of real-world applications, such as medical imaging, satellite imaging, surveillance and security, and astronomical imaging, amongst others.
With the advancement of deep learning techniques in recent years, deep learning-based SR models have been actively explored and often achieve state-of-the-art performance on various benchmarks. A variety of deep learning methods have been applied to solve SR tasks, ranging from the early Convolutional Neural Networks (CNN) to recent cascading residual architectures.
Our team is working on the optimization of a recent state-of-the-art model, the Densely residual Laplacian Network. We developed a simplified version of this model that reduces memory consumption and increases time efficiency without sacrificing the accuracy and providing comparable results. We have thus shown that the task of SR can be efficiently achieved with less complex deep learning architectures.