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This paper presents a tool for video quality assessment that allows the selection of quality (QP), temporal (FPS), and spatial (bitrate) scalability parameters. The proposal integrates traditional metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), together with the perceptual metric Perceptual Image Patch Similarity (LPIPS), which is based on deep neural networks. To validate its effectiveness, a two-phase subjective evaluation methodology was applied. In the first phase, participants assessed videos encoded with the same scalability parameter, showing a strong correspondence between visual perception and objective metrics. In the second phase, different configurations were compared, revealing a preference for high quality and intermediate spatial scalability. Additionally, in experiments with common distortions such as blurring and noise, LPIPS achieved a sensitivity of 73.64%, outperforming PSNR and SSIM in its alignment with human perception. The main contribution of this work is a tool that combines objective and subjective evaluations, enabling a more comprehensive analysis that closely reflects human visual perception.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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