  Banding, also known as staircase-like contours, frequently occurs in flat
areas of images/videos processed by the compression or quantization algorithms.
As undesirable artifacts, banding destroys the original image structure, thus
degrading users' quality of experience (QoE). In this paper, we systematically
investigate the banding image quality assessment (IQA) problem, aiming to
detect the image banding artifacts and evaluate their perceptual visual
quality. Considering that the existing image banding databases only contain
limited content sources and banding generation methods, and lack perceptual
quality labels (i.e. mean opinion scores), we first build the largest banding
IQA database so far, named Banding Artifact Noticeable Database (BAND-2k),
which consists of 2,000 banding images generated by 15 compression and
quantization schemes. A total of 23 workers participated in the subjective IQA
experiment, yielding over 214,000 patch-level banding class labels and 44,371
reliable image-level quality ratings. Subsequently, we develop an effective
no-reference (NR) banding evaluator for banding detection and quality
assessment by leveraging frequency characteristics of banding artifacts. A dual
convolutional neural network is employed to concurrently learn the feature
representation from the high-frequency and low-frequency maps, thereby
enhancing the ability to discern banding artifacts. The quality score of a
banding image is generated by pooling the banding detection maps masked by the
spatial frequency filters. Experiments demonstrate that our banding evaluator
achieves a remarkably high accuracy in banding detection and also exhibits high
SRCC and PLCC results with the perceptual quality labels. These findings unveil
the strong correlations between the intensity of banding artifacts and the
perceptual visual quality, thus validating the necessity of banding quality
assessment.
