Inverse-Tone-Mapped HDR Video Quality Assessment for Broadcast Television: A Comprehensive Dataset and SDR-Referenced Method

Inverse-Tone-Mapped High Dynamic Range Video Quality Assessment (ITM-HDR VQA) plays a pivotal role in evaluating the visual quality of ITM-enhanced HDR videos. The research community tackles this issue from the dataset and method perspectives. However, current ITM-HDR VQA datasets exhibit three key limitations: narrow scene diversity, partial HDR format representation, and inadequate distortion coverage; existing methods face challenges in HDR and SDR domain discrepancy and insufficient ITM-induced quality feature extraction. To bridge these gaps, we introduce a comprehensive ITM HDR Video Quality Assessment dataset tailored to Broadcast Television (BT-ITM-VQA), along with a novel SDR-Referenced Bidirectional Quality Interaction (SDR-R-BQI) method. The BT-ITM-VQA dataset features rich broadcast scenes, multiple HDR-format support of Hybrid Log-Gamma (HLG) and Perceptual Quantizer (PQ), and real-world distortions induced by super-resolution and deinterlacing, providing a systematic foundation for ITM-HDR VQA model development and validation. The SDR-R-BQI method effectively mitigates HDR and SDR discrepancies through luminance dynamic range alignment and color gamut alignment, and then extracts ITM-induced quality alterations by bidirectional, cross-quality-based computation in a unified feature space. Extensive validation on four datasets demonstrates the effectiveness of our newly constructed dataset and proposed method.