A new open AV1 banding video dataset plus CBAND, a fast neural-feature-based no-reference metric that beats prior banding metrics on it and works as a debanding loss.
FS-BAND: A Frequency-Sensitive Banding Detector
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abstract
Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. scenarios, which largely affects the user's quality of experience (QoE). The banding distortion typically appears as relatively small pixel-wise variations in smooth backgrounds, which is difficult to analyze in the spatial domain but easily reflected in the frequency domain. In this paper, we thereby study the banding artifact from the frequency aspect and propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND). The proposed detector is able to generate a pixel-wise banding map with a perception correlated quality score. Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
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Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos
A new open AV1 banding video dataset plus CBAND, a fast neural-feature-based no-reference metric that beats prior banding metrics on it and works as a debanding loss.