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Este artículo presenta una herramienta para la evaluación de la calidad de video, que permite seleccionar parámetros de escalabilidad de calidad (QP), temporal (FPS) y espacial (bitrate). La propuesta integra métricas tradicionales como Peak Signal-to-Noise Ratio (PSNR) y Structural Similarity Index (SSIM), junto con la métrica perceptual Learned Perceptual Image Patch Similarity (LPIPS), basada en redes neuronales profundas. Para validar su efectividad, se aplicó una metodología en dos fases de evaluación subjetiva. En la primera, los participantes evaluaron videos codificados con un mismo parámetro de escalabilidad, mostrando alta correspondencia entre la percepción visual y las métricas. En la segunda, se compararon diferentes configuraciones, evidenciando preferencia por alta calidad y escalabilidad espacial intermedia. Asimismo, en experimentos adicionales con distorsiones comunes (difuminado y ruido), LPIPS alcanzó una sensibilidad del 73.64 %, superando a PSNR y SSIM en su alineación con la percepción humana. La principal contribución de este trabajo es una herramienta que combina evaluaciones objetivas y subjetivas, facilitando un análisis más completo y cercano a la percepción visual humana
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
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