Banding is a visually annoying artifact that frequently occurs along the chain of video acquisition, production, distribution, and display, showing a significant need for improvement in many fields. Thus far, efforts on banding removal are mainly knowledge-driven or merely learning on RGB space, which is either limited by domain knowledge or lacks the consideration for banding in chrominance channels. In this work, we propose a unified deep neural network that explicitly disentangles the luminance and chrominance channels, and simultaneously recovers intensity gradients and color discontinuity from detection-free measurement in an end-to-end manner. Our debanding model is comprised of a luminance restoration network (LR-Net) and a chrominance restoration network (CR-Net). Each of them follows an encoder-decoder architecture, where a cascade of residual blocks is employed to exploit hierarchical non-local features in spatial dimensions for more powerful feature representation. Moreover, we investigate the characteristics of banding artifacts and apply specific loss functions to guide the debanding in different channels, thus boosting the restoration performance. Both qualitative and quantitative experiments show that our model significantly surpasses the existing method in terms of all 7 metrics. Ultimately, our network trained on simulated data exhibits good adaptiveness under various compression scenarios, which further demonstrates the effectiveness of the proposed model.

The proliferation of Artificial Intelligence-Generated Images (AIGIs) has greatly expanded the Image Naturalness Assessment (INA) problem. Different from early definitions that mainly focus on tone-mapped images with limited distortions (e.g., exposure, contrast, and color reproduction), INA on AI-generated images is especially challenging as it owns more diverse contents and could be affected by factors from multiple perspectives, including low-level technical distortions and high-level rationality distortions. In this paper, we take the first step to benchmark and assess the visual naturalness of AI-generated images. First, we construct the AI-Generated Image Naturalness (AGIN) dataset by conducting a large-scale subjective study to collect human opinions on the overall naturalness as well as perceptions from the technical quality and rationality perspectives. AGIN verifies several insights for the first time that naturalness is universally and disparately affected by both technical and rational distortions, while its manifestations vary with different generation tasks. Second, to automatically assess the naturalness of AIGIs that align with human opinions, we propose the Joint Objective Image Naturalness evaluaTor (JOINT). Specifically, JOINT imitates human reasoning in naturalness evaluation by jointly learning technical and rationality features with several specific designs to guide model behavior from respective perspectives. Experiments demonstrate that JOINT significantly outperforms existing methods for providing more subjectively consistent results on naturalness assessment. The dataset can be accessed athttps://github.com/zijianchen98/AGIN.

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.

Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nature of actions within AI-generated video (AIGV). Current action quality assessment (AQA) algorithms predominantly focus on actions from real specific scenarios and are pre-trained with normative action features, thus rendering them inapplicable in AIGVs. To address these problems, we construct GAIA, a Generic AI-generated Action dataset, by conducting a large-scale subjective evaluation from a novel causal reasoning-based perspective, resulting in 971,244 ratings among 9,180 video-action pairs. Based on GAIA, we evaluate a suite of popular text-to-video (T2V) models on their ability to generate visually rational actions, revealing their pros and cons on different categories of actions. We also extend GAIA as a testbed to benchmark the AQA capacity of existing automatic evaluation methods. Results show that traditional AQA methods, action-related metrics in recent T2V benchmarks, and mainstream video quality methods perform poorly with an average SRCC of 0.454, 0.191, and 0.519, respectively, indicating a sizable gap between current models and human action perception patterns in AIGVs. Our findings underscore the significance of action quality as a unique perspective for studying AIGVs and can catalyze progress towards methods with enhanced capacities for AQA in AIGVs.

Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by compression or quantization algorithms. As undesirable artifacts, banding destroys the original image structure, thus inevitably 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, namedBanding 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 rating scores. Subsequently, we develop an effective no-reference (NR) banding evaluator for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts. To be more specific, a dual convolutional neural network (CNN) 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. The experimental results demonstrate that our banding evaluator achieves remarkably high accuracy in banding detection and also exhibits high SRCC and PLCC results with the perceptual quality labels, even without directly learning a regression model for banding quality evaluation. 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. The BAND-2k database and the proposed banding evaluator are available at https://github.com/zijianchen98/BAND-2k.