Future-Proofing Video Quality Assessment: Objective Metrics for Holographic and XR Broadcasting (2026 Outlook)

The introduction of holographic and extended reality (XR) broadcasting has radically changed the working requirements for video quality assessment (VQA). Traditional metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Video Multi-Method Assessment Fusion (VMAF) were designed for two-dimensional video and lack good correspondence to the human perceptual judgment in the immersive media world. In the current work, we perform a systematic assessment of conventional, perceptual and AI-driven metrics using 4K, 8K and holographic xr test sequences with various compression ratios and network noise conditions. Subjective Mean Opinion Scores (MOS) were collected from 30 participants and these were then compared with objective metric outputs to evaluate the validity of each measurement method.Experimental results show a correlation with a percentage of more than 90% with mean opinion scores (MOS), with AI-driven hybrid models * perceptual depth cues, which are much more accurate than conventional approaches at the cost of a high computational cost. In order to counteract the issue of latency, the paper examines the use of edge computing architectures and can therefore demonstrate improved scalability for real-time extended reality (XR) broadcasting. These findings imply that hybrid AI-perceptual approaches represent a future-proof methodology for assessing the objective quality of video content, providing robust and reliable solutions able to cope with the needs of immersive broadcasting ecosystems predicted to occur in 2026 onwards.

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