Limitations of SeedVR2 in Processing Pseudo-HD Videos, and Its Applicability and Optimization Recommendations in AI Video Processing.

SeedVR2 is quite powerful, but it has its own specific scope of application. When is it not applicable? For instance, consider a 4K video that was actually created by crudely upscaling a 360p-resolution video using nearest-neighbor interpolation, resulting in a pseudo-4K video that still essentially retains the information quality of 360p. In such cases, the super-resolution results produced by SeedVR2 often turn out extremely blurry, sometimes indistinguishable from the original video.

How can we maximize SeedVR2's effectiveness then? In such scenarios, it is necessary to first downscale the pseudo-4K video to its actual resolution that matches its information content (e.g., 360p), and then upscale it from 360p to the desired resolution. Only through this process can SeedVR2 achieve optimal performance.

Consequently, with the recent surge in popularity of Sora 2, SeedVR2 is actually less universally applicable compared to FlashVSR. This is primarily because the clarity of videos generated by Sora 2 exhibits a somewhat lottery-like nature—some videos are relatively clear, approaching 720p, while others may struggle to reach even 240p. In such situations, where there is no consistent or determinable downscaling ratio standard, FlashVSR would be a better choice.

Additionally, I would like to suggest that there was a highly robust upscaling algorithm called LDSR from the past couple of years, and the authors might consider including it in their comparative analysis. Based on my subjective evaluation, in terms of upscaling performance for individual images, LDSR is on par with any of the latest algorithms mentioned above and could be listed as one of the benchmark algorithms on the website or in the paper.