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.
Expert Commentary: The Multi-Disciplinary Nature of Perceptual-Oriented Learned Image Compression
In this paper, the authors propose a perceptual-oriented learned image compression method called PO-DKIC, which builds upon their prior research named DKIC. This method aims to enhance the visual quality and compression efficiency of images by incorporating various techniques from different disciplines.
One of the key components of DKIC is the dynamic kernel-based dynamic residual block group, which improves the transform coding process. Transform coding is a fundamental technique used in image and video compression, and by enhancing it, DKIC can achieve better compression results. This aspect of the method relates to multimedia information systems, as it involves optimizing the representation and storage of multimedia data.
Additionally, DKIC utilizes an asymmetric space-channel context entropy model to facilitate the estimation of gaussian parameters. This model takes into account both spatial and channel dependencies in the image data, allowing for more accurate estimation of the statistical properties. Estimating such parameters is crucial for efficient compression algorithms, and the use of this model showcases the integration of concepts from statistics and information theory into image compression.
Building upon DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to further enhance visual quality. PatchGAN is a type of discriminator network commonly used in image synthesis tasks, while LPIPS loss measures perceptual similarity between images based on learned feature representations. These techniques leverage concepts from computer vision and deep learning to improve the visual fidelity of compressed images.
To address the trade-off between compression efficiency and visual quality, the authors formulate the problem as a constrained programming problem and utilize Linear Integer Programming (LIP) for resolution. By formulating the problem in this manner, the method aims to find an optimal solution that maximizes overall perceptual quality under a rate constraint. The application of optimization techniques from operations research and mathematical programming illustrates the interdisciplinary nature of the research.
The experimental results presented in the paper demonstrate the effectiveness of the proposed method. It is shown that PO-DKIC is capable of generating realistic images with richer textures and finer details compared to state-of-the-art image compression techniques. This exemplifies the advancements made in the field of image compression, which is a crucial component of various multimedia systems and applications, including animations, artificial reality, augmented reality, and virtual realities.
In conclusion,
the paper presents a perceptual-oriented learned image compression method that leverages concepts and techniques from multiple disciplines. By incorporating ideas from multimedia information systems, computer vision, deep learning, statistics, and optimization, the proposed method successfully enhances the visual quality and compression efficiency of images. The results obtained highlight the potential impact of this research on various domains that rely on efficient and high-quality image compression, such as animations, artificial reality, augmented reality, and virtual realities.