Blind dehazed image quality assessment (BDQA), which aims to accurately predict the visual quality of dehazed images without any reference information, is essential for the evaluation, comparison, and optimization of image dehazing algorithms. Existing learning-based BDQA methods have achieved remarkable success, while the small scale of DQA datasets limits their performance. To address this issue, in this paper, we propose to adapt Contrastive Language-Image Pre-Training (CLIP), pre-trained on large-scale image-text pairs, to the BDQA task. Specifically, inspired by the fact that the human visual system understands images based on hierarchical features, we take global and local information of the dehazed image as the input of CLIP. To accurately map the input hierarchical information of dehazed images into the quality score, we tune both the vision branch and language branch of CLIP with prompt learning. Experimental results on two authentic DQA datasets demonstrate that our proposed approach, named CLIP-DQA, achieves more accurate quality predictions over existing BDQA methods. The code is available at https://github.com/JunFu1995/CLIP-DQA.

Many super-resolution (SR) algorithms have been proposed to increase image resolution. However, full-reference (FR) image quality assessment (IQA) metrics for comparing and evaluating different SR algorithms are limited. In this work, we propose the Perception-oriented Bidirectional Attention Network (PBAN) for image SR FR-IQA, which is composed of three modules: an image encoder module, a perception-oriented bidirectional attention (PBA) module, and a quality prediction module. First, we encode the input images for feature representations. Inspired by the characteristics of the human visual system, we then construct the perception-oriented PBA module. Specifically, different from existing attention-based SR IQA methods, we conceive a Bidirectional Attention to bidirectionally construct visual attention to distortion, which is consistent with the generation and evaluation processes of SR images. To further guide the quality assessment towards the perception of distorted information, we propose Grouped Multi-scale Deformable Convolution, enabling the proposed method to adaptively perceive distortion. Moreover, we design Sub-information Excitation Convolution to direct visual perception to both sub-pixel and sub-channel attention. Finally, the quality prediction module is exploited to integrate quality-aware features and regress quality scores. Extensive experiments demonstrate that our proposed PBAN outperforms state-of-the-art quality assessment methods.

There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially dual-branch algorithms, to automatically evaluate the visual quality of SR images remains challenging. Existing SR image quality assessment (IQA) metrics based on two-stream networks lack interactions between branches. To address this, we propose a novel full-reference IQA (FR-IQA) method for SR images. Specifically, producing SR images and evaluating how close the SR images are to the corresponding HR references are separate processes. Based on this consideration, we construct a deep Bidirectional Attention Network (BiAtten-Net) that dynamically deepens visual attention to distortions in both processes, which aligns well with the human visual system (HVS). Experiments on public SR quality databases demonstrate the superiority of our proposed BiAtten-Net over state-of-the-art quality assessment methods. In addition, the visualization results and ablation study show the effectiveness of bi-directional attention.

Recently, textual prompt tuning has shown inspirational performance in adapting Contrastive Language-Image Pre-training (CLIP) models to natural image quality assessment. However, such uni-modal prompt learning method only tunes the language branch of CLIP models. This is not enough for adapting CLIP models to AI generated image quality assessment (AGIQA) since AGIs visually differ from natural images. In addition, the consistency between AGIs and user input text prompts, which correlates with the perceptual quality of AGIs, is not investigated to guide AGIQA. In this letter, we propose vision-language consistency guided multi-modal prompt learning for blind AGIQA, dubbed CLIP-AGIQA. Specifically, we introduce learnable textual and visual prompts in language and vision branches of CLIP models, respectively. Moreover, we design a text-to-image alignment quality prediction task, whose learned vision-language consistency knowledge is used to guide the optimization of the above multi-modal prompts. Experimental results on two public AGIQA datasets demonstrate that the proposed method outperforms state-of-the-art quality assessment models.

In no-reference 360-degree image quality assessment (NR 360IQA), graph convolutional networks (GCNs), which model interactions between viewports through graphs, have achieved impressive performance. However, prevailing GCN-based NR 360IQA methods suffer from three main limitations. First, they only use high-level features of the distorted image to regress the quality score, while the human visual system scores the image based on hierarchical features. Second, they simplify complex high-order interactions between viewports in a pairwise fashion through graphs. Third, in the graph construction, they only consider the spatial location of the viewport, ignoring its content characteristics. Accordingly, to address these issues, we propose an adaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN. Specifically, we first design a multi-level viewport descriptor for extracting hierarchical representations from viewports. Then, we model interactions between viewports through hypergraphs, where each hyperedge connects two or more viewports. In the hypergraph construction, we build a location-based hyperedge and a content-based hyperedge for each viewport. Experimental results on two public 360IQA databases demonstrate that our proposed approach has a clear advantage over state-of-the-art full-reference and no-reference IQA models.

We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of learning-based codecs. In this paper, we address the issue of heavy time complexity from the view of arithmetic coding. Prevalent learning-based image compression scheme first maps the natural image into latent representations and then conduct arithmetic coding on quantized latent maps. Previous arithmetic coding schemes define the start and end value of the arithmetic codebook as the minimum and maximum of the whole latent maps, ignoring the fact that the value ranges in most channels are shorter. Hence, we propose to use a channel-adaptive codebook to accelerate arithmetic coding. We find that the latent channels have different frequency-related characteristics, which are verified by experiments of neural frequency filtering. Further, the value ranges of latent maps are different across channels which are relatively image-independent. The channel-adaptive characteristics allow us to establish efficient prior codebooks that cover more appropriate ranges to reduce the runtime. Experimental results demonstrate that both the arithmetic encoding and decoding can be accelerated while preserving the rate-distortion performance of compression model.

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-and-excitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.

Recently, reinforced adaptive bitrate (ABR) algorithms have achieved remarkable success in tile-based 360-degree video streaming. However, they heavily rely on accurate viewport prediction. To alleviate this issue, we propose a hierarchical reinforcement-learning (RL) based ABR algorithm, dubbed 360HRL. Specifically, 360HRL consists of a top agent and a bottom agent. The former is used to decide whether to download a new segment for continuous playback or re-download an old segment for correcting wrong bitrate decisions caused by inaccurate viewport estimation, and the latter is used to select bitrates for tiles in the chosen segment. In addition, 360HRL adopts a two-stage training methodology. In the first stage, the bottom agent is trained under the environment where the top agent always chooses to download a new segment. In the second stage, the bottom agent is fixed and the top agent is optimized with the help of a heuristic decision rule. Experimental results demonstrate that 360HRL outperforms existing RL-based ABR algorithms across a broad of network conditions and quality of experience (QoE) objectives.

Tile-based 360-degree video (360 video) streaming, employed with adaptive bitrate (ABR) algorithms, is a promising approach to offer high video quality of experience (QoE) within limited network bandwidth. Existing ABR algorithms, however, fail to achieve optimal performance in real-world fluctuated network conditions as they heavily rely on unbiased bandwidth predictions. Recently, reinforcement learning (RL) has shown promising potential in generating better ABR algorithms in 2D video streaming. However, unlike existed work in 2D video streaming, directly applying RL in the tile-based 360 video streaming is infeasible due to the resulting exponential decision space. To overcome these limitations, we propose in this paper 360SRL, an improved ABR algorithm employing Sequential RL (360SRL). Firstly, we reduce the decision space of 360SRL from exponential to linear by introducing a sequential ABR decision structure, thus making it feasible to be employed with RL. Secondly, instead of relying on accurate bandwidth predictions, 360SRL learns to make ABR decisions solely through observations of the resulting QoE performance of past decisions. Finally, we compare 360SRL to state-of-the-art ABR algorithms using trace-driven experiments. The experiment results demonstrate that 360SRL outperforms state-of-the-art algorithms with around 12% improvement in average QoE.