sunwei925/CompressedVQA
Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos
This project helps video platform managers and content creators evaluate the visual quality of compressed user-generated content (UGC) videos. By inputting either a compressed video alongside its original (for full-reference) or just the compressed video (for no-reference), it outputs a quality score. This is useful for anyone responsible for video quality on platforms like YouTube, TikTok, or streaming services.
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Use this if you need to objectively measure the perceived quality of compressed user-generated videos, especially after encoding or streaming, to ensure a good viewer experience.
Not ideal if you are assessing video quality for professional, studio-produced content or uncompressed raw footage, as this is specifically tuned for UGC compression artifacts.
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Language
Python
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Apache-2.0
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Last pushed
Aug 18, 2022
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