Traditional vs. Neural Video Codecs: Compression Efficiency, Visual Artifacts, and Quality Analysis Beyond PSNR - Tavares, Leandro; Conceição, Ruhan; Costa, Victor; Agostini, Luciano; Porto, Marcelo; Corrêa, Guilherme (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.16011, 2025)
Video compression technology is rapidly evolving, with end-to-end learned Neural Video Codecs (NVCs) emerging as powerful alternatives to traditional, block-based standards. This paper presents a comparative analysis of two traditional codecs, HEVC and VVC, against two leading NVCs, DCVC-FM and DCVC-RT. We conduct a thorough rate distortion analysis using multiple objective metrics (PSNR, SSIM, VMAF, and LPIPS) and introduce a formal method to identify rate-matched operating points for fair visual comparison. Our results demonstrate that NVCs offer substantial gains not only in signal fidelity but also in perceptual quality, achieving up to 37.82% bitrate savings over HEVC. Furthermore, our analysis reveals fundamental differences in distortion patterns: NVCs excel at preserving structural and color fidelity, producing visually pleasing results free of blocking artifacts, but can sometimes smooth over fine textures. Conversely, traditional codecs are prone to blockiness but can occasionally retain more high-frequency detail. These findings confirm the superior efficiency of NVCs and highlight the need for evaluation methodologies that look beyond PSNR.