In this paper, we extend our prior research named DKIC and propose the perceptual-oriented learned image compression method, PO-DKIC. Specifically, DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the transform coding and an asymmetric space-channel context entropy model to facilitate the estimation of gaussian parameters. Based on DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to maximize the overall perceptual quality under a rate constraint, we formulate this challenge into a constrained programming problem and use the Linear Integer Programming method for resolution. The experiments demonstrate that our proposed method can generate realistic images with richer textures and finer details when compared to state-of-the-art image compression techniques.
An Expert Analysis of Perceptual-Oriented Learned Image Compression
In this paper, the authors introduce a new method called PO-DKIC, which is an extension of their previous research named DKIC. The goal of this research is to develop a perceptual-oriented learned image compression method that produces high-quality compressed images.
DKIC utilizes a dynamic kernel-based dynamic residual block group, which enhances the transform coding process. This allows for better compression ratios while maintaining visual quality. Additionally, an asymmetric space-channel context entropy model is used to facilitate the estimation of Gaussian parameters, further improving the compression process.
Building upon DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance the visual quality of the compressed images. PatchGAN is a discriminator network that focuses on local image structures, while LPIPS loss measures the perceptual similarity between the original and compressed images. By incorporating these techniques, PO-DKIC is able to generate compressed images with richer textures and finer details compared to existing image compression techniques.
One of the strengths of this research is its multi-disciplinary nature. It combines concepts from various fields including image processing, computer vision, and machine learning. This integration of different disciplines allows for a comprehensive approach to image compression, taking into account both visual quality and compression ratios.
In the wider field of multimedia information systems, image compression plays a crucial role in reducing bandwidth requirements and storage space for transmitting and storing images. By developing a perceptual-oriented learned image compression method, PO-DKIC addresses the challenge of balancing compression ratios and visual quality. This has significant implications for applications such as video streaming, where high-quality visuals are desirable without compromising on data transmission efficiency.
Furthermore, PO-DKIC is related to concepts such as animations, artificial reality, augmented reality, and virtual realities. These technologies often rely on efficient image compression algorithms to deliver graphical content in real-time. The ability to generate realistic images with richer textures and finer details can greatly enhance the immersive experience in these virtual environments.
In conclusion, the authors have made significant contributions to the field of image compression with their proposed method, PO-DKIC. By incorporating perceptual-oriented techniques and leveraging concepts from multiple disciplines, they have achieved impressive results in generating high-quality compressed images. The importance of their research extends beyond image compression, as it has implications for multimedia systems and virtual reality applications.