Evaluation of Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) Network

This paper presents an evaluation of the Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) network for enhancing the quality of compressed videos across multiple codecs, including HEVC, VVC, VP9, and AV1. The CF-STIF network leverages spatiotemporal fusion and deep learning techniques to reduce compression artifacts and improve video quality. The evaluation extends existing methods by employing multiple quality metrics such as PSNR, SSIM, LPIPS. The CF-STIF network has been integrated with the Spatio-Temporal Deformable Fusion (STDF) training scheme in order to execute the model. Results demonstrate that CF-STIF achieves the highest quality improvements for HEVC-encoded videos, with an average PSNR increase of 0.813 dB and superior visual quality as measured by SSIM. However, the performance significantly drops for other codecs, particularly AV1, highlighting the need for future adaptations to optimize CF-STIF for diverse compression standards.