In recent years, we have witnessed the exponential growth of research and development explorations on learning-based visual coding. These learned coding approaches, regardless of their focus on image, video, or 3D point cloud, have demonstrated remarkable improvement in coding efficiency compared to traditional solutions developed for decades.

Although international standard organizations such as JPEG, MPEG, etc., have devoted efforts to promote learning-based visual coding techniques, they are often criticized for the lack of reproducibility. Reproducibility concerns the complexity and generalization of the underlying coding model, which is vital for faithfully evaluating the performance of these methods and ensuring the adoption in practical applications. The complexity herein includes computational complexity and memory (space) consumption in both training and inference. The generalization ensures the applicability of the trained model in various data domains, even for unseen data.

This special session seeks original contributions reporting and discussing the reproducibility of recently emerged neural visual coding solutions. It targets a mixed audience of researchers and product developers from several communities, i.e., multimedia coding, machine learning, computer vision, etc. The topics of interest include, but are not limited to:

• Efficient Neural visual coding for image, video, 3D point cloud, etc.
• Model complexity analysis of neural visual coding;
• Model generalization studies of neural visual coding;
• Standardization activity overview and relevant techniques summarization
• Technical alignment of training and testing, e.g., dataset, procedural steps, etc., for fair comparison