CRRC (China)

When a target occupies only a small part of the frame, whole-image quality scores are dominated by background, and public benchmarks carry no target-level human opinion scores. BiCG-Net expands the target box into a nested patch sequence, reads it with 11 frozen image quality models, and subtracts two readings from the same image to cancel the target term and teacher bias by construction. On CSIQ and LIVEC the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, above both the best single teacher and the teacher consensus; frozen and transferred zero-shot from CSIQ to LIVEC it reaches 0.8416, 0.1947 above consensus.

When a target occupies only a small part of the frame, whole-image quality scores are dominated by background, and public benchmarks carry no target-level human opinion scores. BiCG-Net expands the target box into a nested patch sequence, reads it with 11 frozen image quality models, and subtracts two readings from the same image to cancel the target term and teacher bias by construction. On CSIQ and LIVEC the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, above both the best single teacher and the teacher consensus; frozen and transferred zero-shot from CSIQ to LIVEC it reaches 0.8416, 0.1947 above consensus.