UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment

## Abstract

Abstract Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal. Existing methods typically address these tasks independently due to distinct learning objectives. However, they neglect the underlying interconnectedness of both tasks, which hinders the learning of task-agnostic shared representations for human subjective perception.
To confront this challenge, we propose Uni fied vision-language pre-training of Q uality and A esthetics (UniQA), to learn general perceptions of two tasks, thereby benefiting them simultaneously.
Addressing the absence of text in the IQA datasets and the presence of textual noise in the IAA datasets, (1) we utilize multimodal large language models (MLLMs) to generate high-quality text descriptions; (2) the generated text for IAA serves as metadata to purify noisy IAA data. To effectively adapt the pre-trained UniQA to downstream tasks, we further propose a lightweight adapter that utilizes versatile cues to fully exploit the extensive knowledge of the pre-trained model. Extensive experiments demonstrate that our approach attains a new state-of-the-art performance on both IQA and IAA tasks, while concurrently showcasing exceptional zero-shot and few-label image assessment capabilities. The source code will be available at https://github.com/zht8506/UniQA .

## 1 Introduction

The advent of the mobile internet has empowered users worldwide to swiftly and easily share images. Presently, images dominate various social media platforms.
The visual quality and aesthetics serve as crucial factors that aid these platforms in delivering high-quality images to users, thereby enhancing user experience.
Furthermore, these aspects can effectively guide individuals in image photography and editing. Consequently, the Image Quality Assessment (IQA)(^1^11The IQA in this work refers to the no-reference image quality assessment.) and Image Aesthetic Assessment (IAA), which can automatically measure the perceived quality and beauty of an image, are highly important and desired.

IQA and IAA concentrate on distinct aspects of image assessment, with IQA primarily focusing on the distortion level of the image, while IAA is oriented towards evaluating the aesthetic appeal of the image. Due to this divergence, most current works address these tasks independently, enhancing model performance by designing deeper and more sophisticated networks for either the IQA or IAA task. However, these approaches often overlook the underlying commonality between tasks: simulating human subjective perceptions of images.

Specifically, in human subjective evaluation of images, quality and aesthetics exhibit a mutual influence, such that high-quality images tend to possess a higher aesthetic appeal compared to their low-quality counterparts. Thus, the learning process for both tasks not only acquires features unique to themselves but also involves the learning of task-agnostic common representations. This commonality sparks an idea:

Can we develop a foundational model with robust visual assessment perceptions consistent with human to benefit both IQA and IAA tasks?

To achieve this, we propose the Unified pre-training of Quality and Aesthetics (UniQA) to learn general perceptions
that align with human subjective image assessment.
Different from some works [, ] that also attempt to jointly evaluate image quality and aesthetics, our UniQA learns powerful image assessment representations through pre-training, thereby more effectively boosting performance in both IQA and IAA tasks.

Figure: Figure 1: The overview of our method. We leverage MLLMs to generate quality- and aesthetics-related descriptions (Step 1) and utilize the generated data to refine authentic noisy data (Step 2). We conduct unified pre-training to obtain UniQA (Step 3), which can be flexibly applied to both IQA and IAA tasks with a lightweight adapter (Step 4).
Refer to caption: x1.png

A straightforward pre-training solution involves consolidating all IQA and IAA datasets and then training the model to regress towards the mean opinion scores (MOS) annotated by humans.
However, existing datasets show variations in perceptual scales due to differences in subjective testing methodologies [].
As a result, this training strategy makes the model develop a score bias toward larger scale datasets.
Moreover, it may not effectively capture the unique characteristics of IQA and IAA, as the MOS labels cannot be explicitly interpreted.
To this end, we propose to use text descriptions
as a bridge to integrate the two tasks, leveraging the rich and fine-grained semantics inherent in text to provide more auxiliary information.

However, to the best of our knowledge, existing IQA datasets
[, , , , , , ]
lack text descriptions. Conversely, although current IAA datasets [] include text data provided by humans, they often contain considerable noise. Therefore, a top priority is determining how to acquire high-quality image-text data for both IQA and IAA tasks. Recently, multimodal large language models (MLLMs) [, , , , , ] have demonstrated outstanding capabilities in image understanding, which can generate reasonable responses based on images and user instructions. Inspired by this, we propose utilizing MLLMs with tailored prompts to generate quality- and aesthetics-related descriptions for the IQA and IAA datasets, respectively (Step 1 of Fig. 1). As observed in Fig. 2, this approach provides a comprehensive and precise depiction of image quality and aesthetics. Furthermore, we utilize these generated high-quality aesthetics-related descriptions as metadata to refine the raw aesthetic caption dataset (Step 2 of Fig. 1). Finally, we unify the generated and refined image-text datasets to conduct vision-language contrast pre-training (Step 3 of Fig. 1). This results in the pre-trained UniQA with a powerful multimodal image assessment perception.

After pre-training on image-text pairs, we propose a lightweight adapter, namely the Multi-Cue Integration Adapter, to fine-tune the specific dataset of two tasks (Step 4 of Fig. 1).
This adapter uses versatile cues related to image assessment to prompt the pre-trained UniQA, adeptly extracting useful knowledge and comprehensively assessing the image.
With much fewer tunable parameters compared to previous IQA and IAA models, our model outperforms them on both tasks and also demonstrates exceptional performance in zero-shot and few-label image assessment settings.

Our contributions can be summarized as follows:

- ith the assistance of MLLMs, we construct a high-quality image-text dataset about image quality and aesthetics.
Through pre-training on this dataset, we develop UniQA, which effectively learns a general perception of image assessment, promoting the effective and efficient learning of both IQA and IAA tasks.
- •
We propose a novel Multi-Cue Integration Adapter, which integrates various assessment-related cues to fully exploit the extensive knowledge of the pre-trained model with minimal additional parameters.
- •
Extensive experiments show that our method achieves SOTA performance across multiple IQA and IAA datasets. Benefiting from the rich representations learned through pre-training, UniQA demonstrates exceptional zero-shot and few-label image assessment capabilities.

## 2 Related Work

#### Image Quality Assessment.

The rapid development of deep learning has sparked significant interest in their application for IQA.
Early works mainly utilize CNN to solve the IQA problem with various effective techniques, including deeper feature learning networks [], multi-level feature aggregation [], adaptive quality prediction [], and patch-to-picture learning [].
Recently, transformer methods [, , , , , , ] show promising results in the IQA field, which can compensate for the non-local representation ability of CNN.
Despite these impressive breakthroughs, these methods often transfer models pre-trained on classification datasets, such as ImageNet [], to IQA tasks, which may be suboptimal []. Our method can learn more effective representations through joint pre-training on quality-aesthetics image-text data, providing benefits for IQA tasks.

#### Image Aesthetic Assessment.

With the advent of deep learning, IAA methods have evolved from hand-crafted feature extraction [, , , , ] to end-to-end feature learning, marking significant advancements in the IAA domain.
Various techniques have been developed to boost IAA task, including local and global feature integration [, ], graph network [, ]
and theme-aware learning [, ].
Recently, there has been an emergence of multimodal IAA methods [, , ] that incorporate text as auxiliary supervision. However, these methods necessitate the use of text during inference, limiting their flexible application since text is often not easily available. Our method overcomes this limitation by leveraging the rich semantics in text through vision-language pre-training. The pre-trained model can be flexibly applied to the IAA field using only images.

#### Vision-Language Models.

Vision-Language Models (VLMs) [, , , , ] introduce the contrastive learning strategy to acquire image-text correspondences from large-scale image-text pairs.
VLMs have exhibited promising results across multiple tasks [, ], including IQA [, ] and IAA [, ].
Recently, the Multimodal Large Language Models (MLLMs) have garnered increasing research interest, exhibiting remarkable prowess in comprehending image content and reasoning through complex instructions [, , , , , , ]. Most existing MLLMs achieve this by integrating image features with LLM tokens, subsequently fine-tuning the LLM via multimodal instruction tuning. During inference, MLLMs can reason with given images and user instructions, generating text responses by leveraging world knowledge learned during pre-training.

## 3 UniQA: MLLMs-assisted Unified Pre-training

In this section, we first present some preliminaries of related models (Sec. 3.1).
We then describe the process of constructing a unified image-text dataset about quality and aesthetics, with the assistance of MLLMs (Sec. 3.2 and 3.3).
We use this dataset to pre-train the vision-language model (Sec. 3.4), resulting in UniQA with powerful multimodal image assessment perception.

### 3.1 Preliminaries

Vision-language pre-training aims to achieve comprehensive cross-modality understanding by training on web-scale image-text datasets. Benefiting from this large-scale pre-training, CLIP [], a prominent VLM, has demonstrated great promise to assist a broad scope of vision tasks.
Specifically, CLIP comprises an image encoder $f$ and a text encoder $g$, both jointly trained to establish a shared latent space for image and text through contrastive learning.

Given a batch of $N$ paired images and texts $\{x_{I}^{i},x_{T}^{i}\}_{i=1}^{N}$,
CLIP extracts image features $\bm{I}=\{f(x_{I}^{i})\}_{i=1}^{N}$ and text features $\bm{T}=\{g(x_{T}^{i})\}_{i=1}^{N}$ with corresponding encoders.
During pre-training, CLIP seeks to maximize the cosine similarity of paired image and text features, while minimizing the similarity of unmatched pairs.
The contrastive learning objective can be formulated as:

$$ $\displaystyle\mathcal{L}_{\text{image}}=-\mathbb{E}_{I_{i}\sim\bm{I}}\left[ \log\frac{\exp(I_{i}^{\top}T_{i}/\tau)}{\sum_{j=1}^{N}\exp(I_{i}^{\top}T_{j}/ \tau)}\right]$ (1) $\displaystyle\mathcal{L}_{\text{text}}=-\mathbb{E}_{T_{i}\sim\bm{T}}\left[\log \frac{\exp(T_{i}^{\top}I_{i}/\tau)}{\sum_{j=1}^{N}\exp(T_{i}^{\top}I_{j}/\tau) }\right]$ $$

where the $I_{i}$ and $T_{i}$ are the $i$-th features in the batch, and $\tau$ is the temperature parameter. The final contrastive learning loss can be obtained by taking the average: $\mathcal{L}=(\mathcal{L}_{\text{image}}+\mathcal{L}_{\text{text}})/2$. With this training strategy, CLIP can generate aligned features in latent space for paired image-text samples.

### 3.2 Quality- and Aesthetics-related Captioning

Figure: Figure 2: Generated quality- and aesthetics-related captions via MLLMs. The red text refers to MOS-based text guidance. The orange text highlights the quality- and aesthetics-related text.
Refer to caption: x2.png

In order to achieve vision-language pre-training in the field of image assessment, we need to generate text for IQA and IAA datasets since IQA datasets lack text and IAA datasets contain noisy text. Recently, MLLMs have shown advanced performance, so we can use them to generate high-quality textual data for images. Previous studies [, ] have highlighted that it is challenging for MLLMs to directly and accurately perceive the quality and aesthetics of input images, often resulting in positively skewed expressions and strong hallucinations. Thus, to obtain correct and fine-grained descriptions about quality and aesthetics, we provide an open-sourced MLLM ($M_{T}$) with a task-specific prompt $P_{t}$ and a MOS-based text guidance $G$, attaining a large number of captions $Y_{t}$:

$$ $Y_{t}\sim M_{T}(x_{I},P_{t}|G).$ (2) $$

To obtain $G$, we divide images into $5$ levels based on MOS, i.e., {bad, poor, fair, good, perfect} [, , ]. If an image’s MOS ranks in the top $20\%$ of the score range, its level is assigned to perfect. This approach standardizes IQA and IAA datasets with different scales, alleviating the MOS biases of different datasets [].
Additionally, $P_{t}$ is customized for IQA ($P_{IQA}$) and IAA ($P_{IAA}$) tasks, respectively.
As shown in Fig. 2, $P_{IQA}$ involves sharpness, color balance, and noise level [], while $P_{IAA}$ includes content, color, lighting, and composition [].
With these designs, $M_{T}$ is guided towards image assessment and we can obtain generated caption datasets $Y_{IQA}$ and $Y_{IAA}$ for IQA and IAA task, respectively.

Figure: Figure 3: (a) Data purification process: we pre-train CLIP using generated aesthetic captions data $Y_{IAA}$ and then use the pre-trained $\mathrm{CLIP}_{aes}$ to purify data. (b) The proposed adapter: we employ progressive prompts, {bad, poor, fair, good, perfect} with “image”, to prompt the frozen UniQA and a lightweight trainable module to adjust visual features.
Refer to caption: x3.png

### 3.3 Data Purification

In addition to the generated aesthetic captions $Y_{IAA}$, there are also IAA datasets with captions commented by humans [], which directly reflect human aesthetic feelings.
Incorporating comments from various people can offer a more comprehensive description of image aesthetics.
However, while enhancing text diversity, it may introduce noise to the data, as individuals may provide comments unrelated to image aesthetics.
To address this issue, we propose a novel data purification strategy to refine raw captions in the original dataset. This process is illustrated in Fig. 3(a).

Specifically, we introduce Aesthetics-relevance and Informativeness Rank (AIR) to measure the quality of text corresponding to an image. The AIR consists of Aesthetics-relevance Rank (AR) and Informativeness Rank (IR).
To obtain AR, we first pre-train a CLIP model with generated aesthetic data $Y_{IAA}$ to get an aesthetics-aware CLIP model, denoted as $\mathrm{CLIP_{aes}}$.
Then, we employ it to measure the aesthetics relevance score ($s_{A}$) for an image-text pair. Given an image with $n$ captions, AR can be defined as:

$$ $\mathrm{AR}=\mathrm{Rank}(s_{A}^{1}\cdots s_{A}^{n}),\quad s_{A}^{i}=\mathrm{ CLIP_{aes}}(x_{I},x_{T}^{i}),$ (3) $$

where $s_{A}^{i}$ represents the aesthetics relevance score between the $i$-th caption $x_{T}^{i}$ and its corresponding image $x_{I}$. Note that $\mathrm{AR}$ consists of long integers that represent the rank of a caption after sorting by $s_{A}$.
To obtain IR, we simply utilize the sentence length as informativeness score ($s_{I}$) to measure the informativeness of text. Accordingly, for an image with $n$ textual captions, IR can be expressed as:

$$ $\mathrm{IR}=\mathrm{Rank}(s_{I}^{1},\cdots,s_{I}^{n}),\quad s_{I}^{i}=\mathrm{ Length}(x_{T}^{i}),$ (4) $$

where $\mathrm{Length}(\cdot)$ is able to output the length of an input sentence.
As a result, AIR between an image and $n$ captions is:

$$ $\mathrm{AIR}=\mathrm{Rank}((\mathrm{AR}^{1}+\mathrm{IR}^{1}),\cdots,(\mathrm{ AR}^{n}+\mathrm{IR}^{n})).$ (5) $$

We select captions with Top-K ranking AIR to construct a high-quality
aesthetic caption dataset, denoted as $Y_{IAA}^{+}$.
This strategy ensures the preservation of text that is both related to aesthetic perception and rich in information, thereby enhancing the quality and richness of the raw dataset.

### 3.4 Unified Vision-Language Pre-training

So far, we have gotten a high-quality image-text dataset about quality and aesthetics, $Y=Y_{IQA}\cup Y_{IAA}\cup Y_{IAA}^{+}$. Based on it, we pre-train CLIP using Equation 1 to obtain our UniQA. In this way, the model learns general perceptions of image quality and aesthetics,
which can provide potent assessment priors and thus can be effectively applied to both IQA and IAA tasks.

## 4 Adapting Vision-Language Model for IQA and IAA

The pre-trained UniQA contains extensive perception information, which can facilitate downstream assessment tasks in a zero-shot or supervised manner. In this section, we further propose a meticulously designed adapter (Sec. 4.1) and prompt engineering (Sec. 4.2) to enhance the model’s performance.

### 4.1 Multi-Cue Integration Adapter

During pre-training, the model aligns image and assessment-related captions, empowering it with strong comprehension of image quality and aesthetics.
With this foundation model, we can slightly adjust the visual features, efficiently adapting it to score-based image assessment tasks.
To this end, we introduce a lightweight adapter, namely the Multi-Cue Integration Adapter, to adapt visual features and inject rich cues for fine-tuning downstream tasks.
The adapter consists of two key processes: visual feature adaptation and multi-cue integration prediction.

Visual Feature Adaptation.
We add a learnable residual module following the pre-trained image encoder to adjust the visual features so as to adapt to specific assessment datasets. We optimize this module while keeping the image and text backbones frozen, enabling parameter-efficient tuning. The structure of the adapter is illustrated in Fig. 3(b). Let $I$ denote the image features extracted from the frozen image encoder, the visual feature adaptation process can be expressed as:

$$ $\displaystyle I^{\prime}=\mathrm{Normalize}(\mathrm{Adapter}(I)+I)$ (6) $$

where the $\mathrm{Adapter(\cdot)}$ consists of two fully connected layers with a $\mathrm{ReLU}$ activation in between, and $I^{\prime}$ represents the adapted visual features.

Multi-cue Integration Prediction. A straightforward approach to incorporating the CLIP model into perception assessment is to utilize the “good image” as an anchor and take the cosine similarity between the text anchor and a given image as the assessment score. However, this method shows two shortcomings: 1) using the absolute value of similarity as the perception score may not be optimal because it only reflects the semantic similarity between images and texts [, ]; 2) a single prompt may not fully leverage the extensive knowledge of the pre-trained model. Thus, we propose to utilize versatile cues to comprehensively explore the power of the pre-trained UniQA and convert absolute similarity scores into relative values for weighting.

Specifically, we utilize the prompt template “{level} image” and five text levels in Sec. 3.2, i.e., {bad, poor, fair, good, perfect}, to construct prompts.
Next, we calculate the cosine similarity between the normalized text features $\{T_{i}\}_{i=1}^{5}$ of five prompts and adapted visual features $I^{\prime}$, and then use the $\mathrm{Softmax(\cdot)}$ to obtain the related value of five image-text correspondence. These related values will weight the predefined score levels to get the final assessment score. This process can be formulated as follows:

$$ $q=\sum_{i=1}^{5}\frac{c_{i}\exp({{I^{\prime}}^{\top}T_{i}}/\tau)}{\sum_{j=1}^{ 5}\exp({{I^{\prime}}^{\top}T_{i}}/\tau)},$ (7) $$

where $\{c_{i}\}_{i=1}^{5}$ are scores of text levels with progressive values that are set to $\{0.2,0.4,0.6,0.8,1.0\}$; $\tau$ is the temperature parameter and $q$ is the assessment score of the given image.

### 4.2 Prompt Ensemble Strategy

We introduce the prompt ensemble strategy, which incorporates more prompt groups to derive the final assessment score, thereby achieving a more comprehensive understanding of image quality and aesthetics.
For instance, we can use e.g., {extremely blurry, blurry, fair, sharp, extremely sharp} as another five text levels. Now, the final assessment score $q_{f}$ is the average of all prompt groups and it can be described as:

$$ $\displaystyle q_{f}$ $\displaystyle=\frac{\sum_{i=1}^{m}q_{i}}{m},$ (8) $$

where $m$ denotes the number of prompt groups.
This strategy can more fully utilize the multi-modal understanding capabilities of the pre-trained UniQA and demonstrates non-negligible performance improvements in zero-shot (Tab. 4) and few-label supervised learning (Tab. 5). The details of ensemble prompts are attached in supplementary material.

## 5 Experiments

### 5.1 Datasets

We employ the IQA dataset FLIVE [] and the IAA dataset AVA [] for quality- and aesthetics-related captioning, respectively, and AVA-Captions [] to provide authentic aesthetic comments. We evaluate the performance on typical IQA and IAA datasets, including seven IQA datasets and two IAA datasets.

IQA Dataset. For the IQA task, four synthetic datasets, including LIVE [], CSIQ [], TID2013 [], KADID [], and three authentic datasets of CLIVE [], KonIQ [], SPAQ [], are used for performance evaluation. FLIVE [] is an authentic IQA dataset that contains 39,810 images.
We employ an AIGC-generated IQA dataset, AGIQA-3K [], to evaluate the generalization capability of our UniQA.
Details of the datasets can be found in the supplementary material.

IAA Dataset. For the IAA task, we conduct experiments on AVA [] and AADB [] datasets. AVA comprises 250k images, with the test set of 19,928 images.
AADB dataset consists of 10,000 images in total, with 8,500 images for training, 500 images for validation, and 1,000 images for testing.

AVA-Captions Dataset. AVA-Captions [] offer multiple human-annotated comments for each AVA image. To avoid potential data leakage, we strictly follow the official data split of AVA, results in a pre-training image-text dataset comprising 234,090 images paired with 3.0 million captions.

### 5.2 Implementation Details

We use CLIP-B/16 [] as our VLM for pre-training and LLaVA-1.5-7B [, ] as our MLLM for captioning.
We pre-train the model using Adam optimizer [] with a learning rate of 5e-6 and weight decay of 0.2.
The model is trained for 5 epochs with a batch size of 960. We set $K=4$ to refine the AVA-Captions dataset.
We use MSE loss to optimize the adapter on downstream tasks and different training settings according to the task and size of datasets.
More training details are provided in the appendix.
For each IQA dataset, 80% of the images are used for training and the remaining 20% for testing.
We repeat this process 10 times to mitigate the performance bias and the medians of SRCC and PLCC are reported. For the IAA datasets, we follow the standard data splits.

**Table 1: Results on IQA datasets. Black and blue numbers in bold represent the best and second best, respectively. Higher SRCC and PLCC imply better performance.**
|  | TID2013 | CSIQ | KADID | CLIVE | KonIQ | SPAQ |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC |
| WaDIQaM [] | 0.835 | 0.855 | 0.852 | 0.844 | 0.739 | 0.752 | 0.682 | 0.671 | 0.804 | 0.807 | 0.840 | 0.845 |
| DBCNN [] | 0.816 | 0.865 | 0.946 | 0.959 | 0.851 | 0.856 | 0.851 | 0.869 | 0.875 | 0.884 | 0.911 | 0.915 |
| MetaIQA [] | 0.856 | 0.868 | 0.899 | 0.908 | 0.762 | 0.775 | 0.802 | 0.835 | 0.850 | 0.887 | - | - |
| PaQ-2-PiQ [] | 0.862 | 0.856 | 0.899 | 0.902 | 0.840 | 0.849 | 0.844 | 0.842 | 0.872 | 0.885 | - | - |
| HyperIQA [] | 0.840 | 0.858 | 0.923 | 0.942 | 0.852 | 0.845 | 0.859 | 0.882 | 0.906 | 0.917 | 0.911 | 0.915 |
| TReS [] | 0.863 | 0.883 | 0.922 | 0.942 | 0.859 | 0.858 | 0.846 | 0.877 | 0.915 | 0.928 | - | - |
| MUSIQ [] | 0.773 | 0.815 | 0.871 | 0.893 | 0.875 | 0.872 | 0.702 | 0.746 | 0.916 | 0.928 | 0.918 | 0.921 |
| DEIQT [] | 0.892 | 0.908 | 0.946 | 0.963 | 0.889 | 0.887 | 0.875 | 0.894 | 0.921 | 0.934 | 0.919 | 0.923 |
| LIQE [] | - |  | 0.936 | 0.939 | 0.930 | 0.931 | 0.904 | 0.911 | 0.919 | 0.908 | - | - |
| Ours | 0.916 | 0.931 | 0.963 | 0.973 | 0.940 | 0.943 | 0.890 | 0.905 | 0.933 | 0.941 | 0.924 | 0.928 |

**Table 2: Results on AVA dataset.**
| Method | SRCC | PLCC |
| --- | --- | --- |
| NIMA [] | 0.612 | 0.636 |
| MaxViT [] | 0.708 | 0.745 |
| APM [] | 0.709 | - |
| MUSIQ [] | 0.726 | 0.738 |
| MLSP [] | 0.756 | 0.757 |
| TANet [] | 0.758 | 0.765 |
| EAT [] | 0.759 | 0.77 |
| VILA [] | 0.774 | 0.774 |
| Ours | 0.776 | 0.776 |

### 5.3 Main Results

Results on IQA task. Tab. 1 reports the performance of the SOTA IQA methods on six typical IQA datasets.
The results of LIVE [] are presented in the supplementary material due to page limitations.
Our method demonstrates a substantial superiority over existing SOTA models across a diverse range of datasets, fully confirming the effectiveness and excellence of our method in precisely characterizing image quality.

Results on IAA task. We report the experimental results on the AVA [] and AADB [] datasets in Tab. 4 and Tab. 4, respectively. Given that the pre-trained model acquired a unified and robust image assessment perception, it can also achieve SOTA results after fine-tuning on these two datasets.
These results validate that our method can be effectively applied to both IQA and IAA domains.

Figure: Figure 4: The image retrieval results on three dataset with varied prompts. Zoom in for a better view.
Refer to caption: x4.png

**Table 5: SRCC results using few labels for training. ^∗ denotes using ensemble prompts.**
|  | CLIVE | KonIQ | LIVE |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | 50 | 100 | 200 | 50 | 100 | 200 | 50 | 100 | 200 |
| HyperIQA [] | 0.648 | 0.725 | 0.790 | 0.615 | 0.710 | 0.776 | 0.892 | 0.912 | 0.929 |
| TReS [] | 0.670 | 0.751 | 0.799 | 0.713 | 0.719 | 0.791 | 0.901 | 0.927 | 0.957 |
| ResNet50 [] | 0.576 | 0.611 | 0.636 | 0.635 | 0.670 | 0.707 | 0.871 | 0.906 | 0.922 |
| CLIP [] | 0.664 | 0.721 | 0.733 | 0.736 | 0.770 | 0.782 | 0.896 | 0.923 | 0.941 |
| CONTRIQUE [] | 0.695 | 0.729 | 0.761 | 0.733 | 0.794 | 0.821 | 0.891 | 0.922 | 0.943 |
| Re-IQA [] | 0.591 | 0.621 | 0.701 | 0.685 | 0.723 | 0.754 | 0.884 | 0.894 | 0.929 |
| DEIQT [] | 0.667 | 0.718 | 0.812 | 0.638 | 0.682 | 0.754 | 0.920 | 0.942 | 0.955 |
| GRepQ [] | 0.760 | 0.791 | 0.822 | 0.812 | 0.832 | 0.855 | 0.926 | 0.937 | 0.953 |
| Ours | 0.813 | 0.836 | 0.850 | 0.772 | 0.842 | 0.870 | 0.962 | 0.956 | 0.974 |
| Ours^∗ | 0.828 | 0.849 | 0.853 | 0.844 | 0.860 | 0.876 | 0.963 | 0.958 | 0.976 |

### 5.4 Generalization Capability Validation

Tab. 4 evaluate the generalization capability of our model. Unlike previous methods that train on one dataset and test on others, we directly utilize the pre-trained UniQA and textual prompts for image quality assessment. This presents a more challenging setting as the model isn’t optimized on MOS labels.
As observed, our method achieves the best performance on these three datasets.
Notably, our method demonstrates excellent performance on AIGC-generated images AGIQA-3K [], which are markedly different from images of natural scenes. These results demonstrate the strong generalization capability of our UniQA.
Additionally, the UniQA outperforms the original CLIP significantly, proving the effectiveness of our quality- and aesthetics-related pre-training.

We use different text queries to calculate the image-text similarity and rank them to achieve zero-shot image retrieval.
Fig. 4 demonstrates the visualization of the top retrieval results.
We notice that the retrieved results of “good image” exhibit sharp and aesthetically pleasing images, whereas “bad image” prompts retrieve blurry, poor lighting and meaningless images.
These examples provide qualitative evidence of the quality and aesthetic knowledge captured by the pre-trained model.

### 5.5 Data-Efficient Learning

The pre-trained model acquires extensive image assessment knowledge, providing robust priors for downstream tasks. Consequently, our model can deliver impressive performance with limited data. To validate this, we randomly select subsets of 50, 100, and 200 samples from the training set for training and evaluate them on the same test data as full-data supervised learning. We report the median performance across 10 times in Tab. 5. Our method notably outperforms the second-best model GRepQ by a substantial margin, even though GRepQ is specifically designed for data-efficient learning. These results thoroughly demonstrate the potent capability of our method to learn image quality even when only a few labels are available.
Additionally, several insightful observations can be drawn from Tab. 5. Firstly, the prompt ensemble strategy markedly enhances model performance in the data-efficient setting. This is attributed to its ability to more fully leverage the extensive knowledge of the pre-trained model. Secondly, the impact of prompt ensemble is slight on synthetic datasets. This is likely due to the limited image variety within synthetic datasets, making a single prompt sufficient for such scenarios.

### 5.6 Ablation Studies

Impact of different pre-training data.
Tab. 6 shows the effect of different pre-training data.
We observe that unified pre-training achieves the optimal performance on both tasks.
In addition, we derive some meaningful observations.
First, using either the generated $Y_{IQA}$ or $Y_{IAA}$ improves the performance of both IQA and IAA tasks, proving the mutual benefit of these two tasks and the effectiveness of MLLMs captioning.
Second, unifying $Y_{IQA}$ and $Y_{IAA}$ datasets does not lead to significant improvements. We believe this is because the MLLMs-generated text tends to have similar sentence structures [] and perception representation, limiting the diversity provided for multimodal learning. Third, pre-training with refined authentic $Y_{IAA}^{+}$ shows significant improvement on two tasks, reflecting that human-annotated comments can provide a more comprehensive and effective representation for the model.

**Table 6: Ablation on IQA (CLIVE and KonIQ) and IAA (AVA) datasets with SRCC metrics.**
| Ablation type | CLIVE | KonIQ | AVA |  |  |
| --- | --- | --- | --- | --- | --- |
| Ablation on different pre-training data |  |  |  |  |  |
| $Y_{IQA}$ | $Y_{IAA}$ | $Y_{IAA}^{+}$ |  |  |  |
| $\times$ | $\times$ | $\times$ | 0.865 | 0.907 | 0.748 |
| $\checkmark$ | $\times$ | $\times$ | 0.871 | 0.914 | 0.755 |
| $\times$ | ✓ | $\times$ | 0.871 | 0.917 | 0.755 |
| ✓ | ✓ | $\times$ | 0.874 | 0.918 | 0.756 |
| $\times$ | $\times$ | ✓ | 0.875 | 0.928 | 0.773 |
| $\times$ | ✓ | ✓ | 0.877 | 0.930 | 0.774 |
| ✓ | ✓ | ✓ | 0.890 | 0.933 | 0.776 |
| Ablation on data purification strategy |  |  |  |  |  |
| w/o Strategy | 0.876 | 0.929 | 0.772 |  |  |
| IR Strategy | 0.879 | 0.931 | 0.774 |  |  |
| AR Strategy | 0.885 | 0.930 | 0.774 |  |  |
| AIR Strategy | 0.890 | 0.933 | 0.776 |  |  |
| Ablation on the proposed adapter |  |  |  |  |  |
| Single Prompt | 0.705 | 0.920 | 0.765 |  |  |
| Antonym Prompt | 0.875 | 0.928 | 0.771 |  |  |
| Ours adapter | 0.890 | 0.933 | 0.776 |  |  |
| Ablation on different MLLMs |  |  |  |  |  |
| LLaVA-v1.5-7B | 0.871 | 0.914 | 0.755 |  |  |
| LLaVA-v1.5-13B | 0.872 | 0.914 | 0.757 |  |  |
| Sphinx | 0.874 | 0.916 | 0.758 |  |  |
| QWen-VL | 0.870 | 0.913 | 0.757 |  |  |
| LLaVa-7B+QWen | 0.875 | 0.916 | 0.758 |  |  |
| Sphinx+QWen | 0.877 | 0.918 | 0.759 |  |  |

Effectiveness of data purification strategy.
The second part of Tab. 6 illustrates the ablation study of the data purification strategy. It can be observed that employing either AR or IR strategy to purify data can improve the model’s performance.
These results validate the benefit of obtaining aesthetically relevant and semantically rich textual descriptions for the model.
Finally, when combining these two strategies, it achieves the best performance.

Effectiveness of the Multi-Cue Integration Adapter.
The third part of Tab. 6 shows the ablation study of the proposed adapter. “Single Prompt” denotes using the similarity between the text “good image” and images as the assessment score directly, while “Antonym Prompt” represents using the relative weights of texts “good image” and “bad image” to weight the predefined score. It is evident that the “Single Prompt” is considerably inferior to the “Antonym Prompt”, showing the limitations of using semantic similarity as score directly. Our method integrates more cues into the “Antonym Prompt” to comprehensively assess images, thereby achieving optimal performance.

Effectiveness of the Multi-Cue Integration Adapter.
The bottom part of Tab. 6 presents the ablation study of various MLLMs. We generate $Y_{IQA}$ via different MLLMs for pre-training. It is evident that using different MLLMs exhibits similar performance, while ensembling different MLLMs can boost performance. This indicates that MLLMs are capable of generating accurate captions with our text-guided prompt, and enhancing caption diversity can further improve performance. Considering resource limitations, we use LLaVa-7B and will integrate more MLLMs in the future.

## 6 Conclusion and Discussion

This paper introduces UniQA, which leverages unified vision-language pre-training to address quality and aesthetic assessment problems concurrently.
We construct a high-quality image-text dataset about quality and aesthetics with the assistance of MLLMs. Through large-scale pre-training on this dataset, UniQA learns shared and effective representations of IQA and IAA tasks, benefiting both tasks.
Additionally, we propose a Multi-Cue Integration Adapter to effectively adapt the pre-trained UniQA to downstream assessment tasks. Our method achieves state-of-the-art performance on both IQA and IAA tasks, and demonstrates powerful zero-shot and few-label image assessment capabilities.

Limitations and future work.
MLLMs often generate captions with similar sentence structures and semantic expressions, restricting their ability to provide diverse and enriched representations for multimodal learning.
Future work will explore other techniques to address this issue, including integrating various MLLMs for captioning and employing in-context learning methods.

## Appendix A Discussion about the AIR

We propose the Aesthetics-relevance and Informativeness Rank (AIR) to select the high-quality texts corresponding to an image. The AIR can be expressed as follows:

$$ $\mathrm{AIR}=\mathrm{Rank}((\mathrm{AR}^{1}+\mathrm{IR}^{1}),\cdots,(\mathrm{ AR}^{n}+\mathrm{IR}^{n})).$ (9) $$

where $\mathrm{AR}$ and $\mathrm{IR}$ denote the Aesthetics-relevance Rank and Informativeness Rank, respectively; $n$ is the number of comments corresponding to an image.
For simplicity, we directly take the summation of IR and AR to reflect the semantic relevance and richness of the text. In fact, we can introduce two factors ($\alpha$ and $\beta$) to purify the data more flexibly. Now, the modified $\mathrm{AIR_{m}}$ can be formulated as:

$$ $\mathrm{AIR_{m}}=\mathrm{Rank}((\alpha\mathrm{AR}^{1}+\beta\mathrm{IR}^{1}), \cdots,(\alpha\mathrm{AR}^{n}+\beta\mathrm{IR}^{n})).$ (10) $$

For instance, we can use large $\alpha$ for the highly noisy data. With this strategy, we can more flexibly purify data based on data quality.

**Table 7: Details of different IQA datasets.**
| Dataset | Dataset Type | Dataset Size | Number of distortions |
| --- | --- | --- | --- |
| LIVE [] | Synthetic | 799 | 5 |
| CSIQ [] | Synthetic | 866 | 5 |
| TID2013 [] | Synthetic | 3,000 | 24 |
| KADID [] | Synthetic | 10,125 | 25 |
| CLIVE [] | Authentic | 1,162 | - |
| KonIQ [] | Authentic | 10,073 | - |
| SPAQ [] | Authentic | 11,000 | - |
| FLIVE [] | Authentic | 39,810 | - |
| AGIQA-3K [] | Authentic | 2,982 | - |

## Appendix B Details of Datasets and Evaluation Criteria

We list the details of the datasets used in our work in Tab. 7, including the dataset type, dataset size and number of distortion types. Since the distortions of authentic datasets are diverse, their number cannot be counted.

We employ Spearman’s Rank-order Correlation Coefficient (SRCC) and Pearson’s Linear Correlation Coefficient (PLCC) as criteria to measure the performance of IQA and IAA models. They reflect the prediction monotonicity and prediction accuracy of the model, respectively. Both SRCC and PLCC range from 0 to 1. Higher values of SRCC and PLCC indicate better performance.

**Table 8: Training settings for different datasets.**
| Dataset | Task | Epoch | Batch size | Learning rate |
| --- | --- | --- | --- | --- |
| LIVE [] | IQA | 50 | 8 | 2e-4 |
| CSIQ [] | IQA | 50 | 8 | 2e-4 |
| TID2013 [] | IQA | 20 | 8 | 2e-4 |
| KADID [] | IQA | 20 | 8 | 2e-4 |
| CLIVE [] | IQA | 50 | 8 | 2e-4 |
| KonIQ [] | IQA | 20 | 8 | 2e-4 |
| SPAQ [] | IQA | 20 | 8 | 2e-4 |
| AVA [] | IAA | 20 | 128 | 5e-4 |
| AADB [] | IAA | 20 | 8 | 5e-4 |

**Table 9: Text prompts used in zero-shot and few-label learning.**
| Task | Prompt |
| --- | --- |
| CLIVE, KonIQ, LIVE | {bad, poor, fair, good, perfect} with image |
| {extremely blurry, blurry, |  |
| fair, sharp, extremely sharp} with image |  |
| {extremely noisy, noisy, fair, |  |
| noise-free, extremely noise-free} with image |  |
| {extremely low-quality, low-quality, fair, |  |
| high-quality, extremely high-quality} with image |  |
| AGIQA-3K | {bad, poor, fair, good, perfect} with image |
| {bad, poor, fair, good, perfect} with content |  |

## Appendix C More Implementation Details

For the pre-training, we employ the same training strategy as CLIP [] to pre-train our UniQA. The pre-training is resource-friendly and takes less than an hour at a time.
When fine-tuning the adapter for downstream assessment tasks, we use different training settings according to the task and size of the dataset. Tab. 8 shows the detailed training setting for the different datasets.
We follow the typical training strategy to fine-tune each dataset, including random cropping and random horizontal flipping. Since different datasets have different MOS scales, we scale their range to [0, 1] through normalization. During inference, we typically crop an input image into 10 image patches and take their average as the quality score of this image [, ]. We use the resolution of $224\times 224$ for training and testing. All experiments are conducted on two A100 GPUs.

## Appendix D Prompt Ensemble

**Table 10: Results on LIVE dataset []. Black and blue numbers in bold represent the best and second best, respectively. Higher SRCC and PLCC imply better performance.**
|  | LIVE |  |
| --- | --- | --- |
| Method | SRCC | PLCC |
| DIIVINE [] | 0.892 | 0.908 |
| BRISQUE [] | 0.929 | 0.944 |
| ILNIQE [] | 0.902 | 0.906 |
| BIECON [] | 0.958 | 0.961 |
| MEON [] | 0.951 | 0.955 |
| WaDIQaM [] | 0.960 | 0.955 |
| DBCNN [] | 0.968 | 0.971 |
| MetaIQA [] | 0.960 | 0.959 |
| PaQ-2-PiQ [] | 0.959 | 0.958 |
| HyperIQA [] | 0.962 | 0.966 |
| TReS [] | 0.969 | 0.968 |
| MUSIQ [] | 0.940 | 0.911 |
| DACNN [] | 0.978 | 0.980 |
| DEIQT [] | 0.980 | 0.982 |
| LIQE [] | 0.970 | 0.951 |
| Ours | 0.981 | 0.983 |

When applying our UniQA to zero-shot and few-label settings, prompt ensemble is a useful strategy to improve performance. Tab. 9 shows the prompt groups used in these two settings. Note that the prompts used in AGIQA-3K are different from other IQA datasets.
This is because distortions in AIGC-generated images and authentic images tend to be different. For example, distortions in authentic images may come from camera shake. However, distortions in AIGC-generated images typically come from low-quality content, such as meaningless content and distorted poses.
Therefore, we use “content” to prompt the pre-trained multimodal model for the AGIQA-3K dataset.

**Table 11: PLCC performance comparison of our method with other NR-IQA methods trained using few labels. ^∗ denotes using ensemble prompts.**
|  | LIVEC | KonIQ | LIVE |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | 50 | 100 | 200 | 50 | 100 | 200 | 50 | 100 | 200 |
| HyperIQA [] | 0.689 | 0.755 | 0.806 | 0.650 | 0.758 | 0.807 | 0.903 | 0.922 | 0.931 |
| TReS [] | 0.702 | 0.776 | 0.813 | 0.740 | 0.748 | 0.824 | 0.916 | 0.948 | 0.960 |
| ResNet50 [] | 0.580 | 0.629 | 0.660 | 0.661 | 0.693 | 0.716 | 0.872 | 0.908 | 0.920 |
| CLIP [] | 0.676 | 0.739 | 0.758 | 0.749 | 0.790 | 0.802 | 0.891 | 0.924 | 0.942 |
| CONTRIQUE [] | 0.693 | 0.736 | 0.777 | 0.743 | 0.801 | 0.832 | 0.892 | 0.922 | 0.944 |
| Re-IQA [] | 0.620 | 0.650 | 0.701 | 0.689 | 0.693 | 0.757 | 0.876 | 0.892 | 0.931 |
| DEIQT [] | 0.695 | 0.739 | 0.818 | 0.670 | 0.707 | 0.778 | 0.916 | 0.942 | 0.957 |
| GRepQ [] | 0.772 | 0.798 | 0.835 | 0.793 | 0.816 | 0.840 | 0.929 | 0.936 | 0.957 |
| Ours | 0.819 | 0.854 | 0.866 | 0.815 | 0.861 | 0.890 | 0.952 | 0.959 | 0.970 |
| Ours^∗ | 0.826 | 0.847 | 0.869 | 0.857 | 0.883 | 0.893 | 0.963 | 0.962 | 0.973 |

## Appendix E More Experimental Results

### E.1 Comparison Results on LIVE

Tab .10 shows the comparison results with other methods on LIVE dataset []. We can observe that our method also achieves state-of-the-art (SOTA) performance, verifying the effectiveness of our method.

### E.2 PLCC Comparison in the Data-efficient Setting

The Pearson’s Linear Correlation Coefficient (PLCC) comparisons for our method against other IQA methods corresponding to the table in the main paper are provided in Tab .11. We note that our method outperforms all other methods in terms of PLCC metric.

### E.3 More Comparison Results on IQA Datasets

To demonstrate the superiority of our method more comprehensively, we present more comparison results on the typical IQA datasets in Tab. 12.

### E.4 More Results of Ablation Study

Tab. 13 shows the SRCC and PLCC results of the ablation study.

## Appendix F Details and Discussion of MLLMs Captioning

Details of MLLMs Captioning.
We use different numbers of captions for IQA and IAA tasks.
Considering that the IQA data does not have textual descriptions, we generate three captions with different prompts via MLLMs for the IQA datasets. This method can improve the text diversity of IQA image-text data.
For the IAA dataset, we generate one caption for each image because IAA datasets have a large amount of authentic text data.
Details of the prompts for quality-related captioning are shown in Fig. 5.

**Table 12: Results on IQA datasets. Black and blue numbers in bold represent the best and second best, respectively. Higher SRCC and PLCC imply better performance.**
|  | TID2013 | CSIQ | KADID | CLIVE | KonIQ | SPAQ |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC |
| DIIVINE [] | 0.643 | 0.567 | 0.804 | 0.776 | 0.413 | 0.435 | 0.588 | 0.591 | 0.546 | 0.558 | 0.599 | 0.600 |
| BRISQUE [] | 0.626 | 0.571 | 0.812 | 0.748 | 0.528 | 0.567 | 0.629 | 0.629 | 0.681 | 0.685 | 0.809 | 0.817 |
| ILNIQE [] | 0.521 | 0.648 | 0.822 | 0.865 | 0.534 | 0.558 | 0.508 | 0.508 | 0.523 | 0.537 | 0.712 | 0.713 |
| BIECON [] | 0.717 | 0.762 | 0.815 | 0.823 | 0.623 | 0.648 | 0.613 | 0.613 | 0.651 | 0.654 | - | - |
| MEON [] | 0.808 | 0.824 | 0.852 | 0.864 | 0.604 | 0.691 | 0.697 | 0.710 | 0.611 | 0.628 | - | - |
| WaDIQaM [] | 0.835 | 0.855 | 0.852 | 0.844 | 0.739 | 0.752 | 0.682 | 0.671 | 0.804 | 0.807 | 0.840 | 0.845 |
| DBCNN [] | 0.816 | 0.865 | 0.946 | 0.959 | 0.851 | 0.856 | 0.851 | 0.869 | 0.875 | 0.884 | 0.911 | 0.915 |
| MetaIQA [] | 0.856 | 0.868 | 0.899 | 0.908 | 0.762 | 0.775 | 0.802 | 0.835 | 0.850 | 0.887 | - | - |
| PaQ-2-PiQ [] | 0.862 | 0.856 | 0.899 | 0.902 | 0.840 | 0.849 | 0.844 | 0.842 | 0.872 | 0.885 | - | - |
| HyperIQA [] | 0.840 | 0.858 | 0.923 | 0.942 | 0.852 | 0.845 | 0.859 | 0.882 | 0.906 | 0.917 | 0.911 | 0.915 |
| TReS [] | 0.863 | 0.883 | 0.922 | 0.942 | 0.859 | 0.858 | 0.846 | 0.877 | 0.915 | 0.928 | - | - |
| MUSIQ [] | 0.773 | 0.815 | 0.871 | 0.893 | 0.875 | 0.872 | 0.702 | 0.746 | 0.916 | 0.928 | 0.918 | 0.921 |
| DACNN [] | 0.871 | 0.889 | 0.943 | 0.957 | 0.905 | 0.905 | 0.866 | 0.884 | 0.901 | 0.912 | 0.915 | 0.921 |
| DEIQT [] | 0.892 | 0.908 | 0.946 | 0.963 | 0.889 | 0.887 | 0.875 | 0.894 | 0.921 | 0.934 | 0.919 | 0.923 |
| LIQE [] | - |  | 0.936 | 0.939 | 0.930 | 0.931 | 0.904 | 0.911 | 0.919 | 0.908 | - | - |
| Ours | 0.916 | 0.931 | 0.963 | 0.973 | 0.940 | 0.943 | 0.890 | 0.905 | 0.933 | 0.941 | 0.924 | 0.928 |

Figure: Figure 5: Details of the prompts of quality-related captioning. We generate three captions for each IQA image using different prompts.
Refer to caption: x5.png

**Table 13: Ablation experiments on two IQA datasets (CLIVE and KonIQ) and one IAA dataset (AVA). Different ablations are distinguished by different backgrounds for better viewing.**
|  |  |  | CLIVE | KonIQ | AVA |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Ablation type | SRCC | PLCC | SRCC | PLCC | SRCC | PLCC |  |  |
| Ablation on different pre-training data |  |  |  |  |  |  |  |  |
| $Y_{IQA}$ | $Y_{IAA}$ | $Y_{IAA}^{+}$ |  |  |  |  |  |  |
| $\times$ | $\times$ | $\times$ | 0.865 | 0.886 | 0.907 | 0.924 | 0.748 | 0.747 |
| $\checkmark$ | $\times$ | $\times$ | 0.871 | 0.898 | 0.914 | 0.932 | 0.755 | 0.755 |
| $\times$ | ✓ | $\times$ | 0.871 | 0.895 | 0.917 | 0.932 | 0.755 | 0.756 |
| ✓ | ✓ | $\times$ | 0.874 | 0.892 | 0.918 | 0.932 | 0.756 | 0.757 |
| $\times$ | $\times$ | ✓ | 0.875 | 0.895 | 0.928 | 0.937 | 0.773 | 0.774 |
| $\times$ | ✓ | ✓ | 0.877 | 0.895 | 0.930 | 0.939 | 0.774 | 0.774 |
| ✓ | ✓ | ✓ | 0.890 | 0.905 | 0.933 | 0.941 | 0.776 | 0.776 |
| Ablation on data purification strategy |  |  |  |  |  |  |  |  |
| w/o Strategy | 0.876 | 0.899 | 0.929 | 0.940 | 0.772 | 0.771 |  |  |
| IR Strategy | 0.879 | 0.898 | 0.931 | 0.941 | 0.774 | 0.774 |  |  |
| AR Strategy | 0.885 | 0.901 | 0.930 | 0.942 | 0.774 | 0.773 |  |  |
| AIR Strategy | 0.890 | 0.905 | 0.933 | 0.941 | 0.776 | 0.776 |  |  |
| Ablation on the proposed adapter |  |  |  |  |  |  |  |  |
| Single Prompt | 0.705 | 0.720 | 0.920 | 0.931 | 0.765 | 0.765 |  |  |
| Antonym Prompt | 0.875 | 0.897 | 0.928 | 0.938 | 0.771 | 0.772 |  |  |
| Ours adapter | 0.890 | 0.905 | 0.933 | 0.941 | 0.776 | 0.776 |  |  |
| Ablation on different MLLMs |  |  |  |  |  |  |  |  |
| LLaVA-v1.5-7B | 0.871 | 0.898 | 0.914 | 0.932 | 0.755 | 0.755 |  |  |
| LLaVA-v1.5-13B | 0.872 | 0.897 | 0.914 | 0.929 | 0.757 | 0.759 |  |  |
| Sphinx | 0.874 | 0.902 | 0.916 | 0.931 | 0.758 | 0.758 |  |  |
| QWen-VL | 0.870 | 0.895 | 0.913 | 0.930 | 0.757 | 0.758 |  |  |
| LLaVa-7B+QWen | 0.875 | 0.899 | 0.916 | 0.930 | 0.758 | 0.757 |  |  |
| Sphinx+QWen | 0.877 | 0.908 | 0.918 | 0.934 | 0.759 | 0.760 |  |  |

Effectiveness of text guidance.
We visualize the MLLMs-generated captions with/without text guidance to evaluate the effectiveness of text guidance. We take the captioning for IQA datasets as examples. As shown in Fig. 10, when the quality of image is high, the MLLMs can output correct caption (see example 1). However, we can observe that the MLLMs will generate wrong captions when low-quality images are sent to MLLMs and the text guidance is not provided (see example 2-4 of Fig. 10). As a result, the text guidance is useful and necessary for MLLMs captioning.

## Appendix G More visualization

### G.1 Grad-CAM visualization of Different Pre-training

Fig. 6 illustrates the Grad-CAM [] visualization of different pre-training. We can notice that after quality-related pre-training, the model pays more attention to blurred subjects and noisy backgrounds. This effect becomes more pronounced with unified pre-training, underscoring the advantages of such a unified approach.

### G.2 More Examples of Zero-shot Retrieval

We show more examples of zero-shot retrieval in Fig. 7, Fig. 8 and Fig. 9. We explore the model’s retrieval capabilities in terms of color and light. We can observe that the images of “Good color” show harmonious and vivid colors while the images of “Bad color” present messy colors. For the retrieval on light, the images of “Good light” are clear and well-lit and the images of “Bad light” demonstrate dimly lit environments and poor lighting. These visualization results further demonstrate the strong capability of our model in understanding concepts related to quality and aesthetics.

Figure: Figure 6: Grad-CAM [] visualization of different pre-training for prompt “blurry image”. Through pre-training, the model focuses more on noisy objects and backgrounds.
Refer to caption: x6.png

Figure: Figure 7: More image retrieval results with various text as queries on CLIVE [].
Refer to caption: x7.png

Figure: Figure 8: More image retrieval results with various text as queries on KonIQ [].
Refer to caption: x8.png

Figure: Figure 9: More image retrieval results with various text as queries on AGIQA-3K [].
Refer to caption: x9.png

Figure: Figure 10: Example of captioning with/without text guidance. The red text refers to MOS-based text guidance. The orange text highlights the correct quality-related caption. The green text indicates the incorrect quality-related caption without text guidance.
Refer to caption: x10.png

### G.3 Visualization of Data Purification Strategy

In Fig. 11, we visualize the comments sorted by the proposed strategies. As observed, the Aesthetics-relevance Rank (AR) can sort the aesthetics-related comments first. Further integrating the Informativeness Rank (IR), we can obtain aesthetically relevant comments with rich semantics.

### G.4 More Examples of Text Generated by MLLMs

In Fig. 12, we show more examples of the captions generated by MLLMs. We can observe that with our prompt design, MLLMs can output correct fine-grained quality- and aesthetics-related descriptions.

Figure: Figure 11: The comments sorted using different strategies. With our proposed strategy, we can obtain aesthetically relevant and informative comments. The order of comments is determined by the corresponding strategy. Gray sentences indicate being filtered.
Refer to caption: x11.png

Figure: Figure 12: More examples of quality- and aesthetics-related captioning via MLLMs. The red text refers to MOS-based text guidance. The orange text highlights the quality- and aesthetics-related text.
Refer to caption: x12.png

## Peer Reviews

confidence: 5
contribution: 3
initial_rating: 8
presentation: 3
questions: 1. Motivation Concerns: The motivation for jointly considering IQA and IAA tasks could be questioned. Is there a urgent or practical need to address IQA and IAA together? Do these tasks mutually benefit each other, or does their combination offer real-world value beyond mere metric improvements?
2. Combined Dataset Training: The authors claim that existing combined dataset training fails to learn mutually beneficial representations shared by both tasks. In fact, the authors construct three datasets (YIQA, YIAA and Y+IAA) to train a CLIP model, which resembles the challenge they raised. This raises questions about the rationality of their motivation and calls for concrete evidence to support it.
3. Noise in human- annotated IAA Datasets: The authors argue that textual noise in IAA datasets negatively impact prediction performance. It would be more convincing if they specified what constitutes " textual noise " and clarified the specific negative impacts. In addition, the claim that MLLM-generated description can purify " textual noise " is unconvincing, as only one MLLM model was used in description generation. The model’s biases and possible overuse of irrelevant words are not addressed, making this assumption lack strong justification.
4. Common feature representation: The authors suggest that the proposed method can extract common representations from IQA and IAA tasks. Visual evidence supporting this claim would strengthen their argument.
5. Informativeness Rank (IR): Using sentence length as the metric for Informativeness Rank might be biased. The number of irrelevant or potentially harmful words within a sentence should not be overlooked. 
6. Related Work: The IQA and IAA methods discussed in the related work section are relatively outdated. Adding more recent and representative algorithms would improve this section.
7. Experimental Comparisons and Visualization Limitations: Some newest comparison methods are required. Additionally, the visualization in Fig. 5 fails to demonstrate that the proposed method focuses more on noisy objects and backgrounds. The examples tend to provide “blurry image”, but the sample mostly depicts a blurred background with clear objects, which does not align with the stated objective.
soundness: 2
strengths: 1. This paper includes comprehensive datasets, encompassing nearly all existing IQA and IAA datasets, providing robust validation for the effectiveness of the proposed method.
2. The methodology of the algorithm is described in a clear and straightforward manner, with concise and easily understandable language.
3. The paper presents an extensive set of experiments and rich visualizations, thoroughly validating each module within the algorithm's design.
summary: This paper proposes a unified evaluation model to handle both image quality assessment (IQA) and image aesthetic assessment (IAA) tasks simultaneously. Specifically, the proposed method first leverages the existing multimodal large language model (MLLM) to generate IQA (YIQA), IAA (YIAA) datasets with generated descriptions, and purify IAA dataset (Y+IAA) annotated by human’s comments. Then, the proposed method train the CLIP model based on YIQA, YIAA and Y+IAA. Finally, an multi-cue integration adapter is designed to allow the pre-trained CLIP to adapt to specific datasets.
weaknesses: 1. The paper’s motivation appears to lack practical significance or has not been convincingly demonstrated.
2. While the proposed approach is intricate, its actual innovation is minimal.
3. The related work section includes methods that are either outdated or lack representativeness in the current IQA and IAA research.
4. Beyond metric improvements, the proposed method lacks substantial inspirational value for future studies.
5. The paper is missing some essential experiments that could substantiate its motivations.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. In Eq. (5), simply summing AR and IR may not be the optimal approach. Assigning different weights to AR and IR and conducting relevant experiments to determine the best weight parameters could yield better results.
2. In Line 103, the statement "achieving SRCC values of 0.828 (vs. 0.764 on CLIVE) and 0.844 (vs. 0.812 on KonIQ)" is somewhat confusing, as it is unclear which method these results are being compared against.
soundness: 2
strengths: 1. In this paper, a high-quality image-text dataset about image quality and aesthetics is constructed based on the assistance of MLLMs, which is valuable．
2．The organizational structure of the article is clear and the content is complete. The writing is clear and easy to follow.　
3. The motivation to "extract mutually beneficial and effective representations for both IQA and IAA tasks" in this paper is plausible.
4. This paper proposes an effective data purification strategy that refines the raw aesthetic caption dataset, providing valuable insights for data organization and cleaning in the IAA field.
summary: This paper aims to leverage unified vision-language pre-training to address quality and aesthetic assessment problems concurrently, and proposes a method named UniQA. On the one hand, this paper constructs a high-quality image-text dataset about quality and aesthetics with the assistance of MLLMs. On the other hand, UniQA learns the shared representations of IQA and IAA tasks by pre-training on the constructed dataset.  Additionally, a Multi-Cue Integration Adapter is proposed in UniQA for downstream assessment tasks.
weaknesses: 1．The authors highlight that the motivation of this paper is to "extract mutually beneficial and effective representations for both IQA and IAA tasks." However, throughout the paper, neither the proposed dataset nor the proposed method fully explore the mutually beneficial representations for IQA and IAA tasks; instead, they only address the creation of effective representations for these tasks. Specifically, the method proposed in this paper learns a shared feature representation for IQA and IAA tasks, without proving how the representation is "mutually beneficial" which is somewhat disappointing. It is suggested to provide specific experiments or analyses that demonstrate how the learned representations benefit both IQA and IAA tasks mutually. 
2．The experiments in this paper are not sufficiently comprehensive. First, the compared methods are relatively outdated, lacking comparisons with more recent works in 2024, such as [1][2]. Additionally, the ablation study only focuses on the IQA task, without any ablation analysis for the IAA task, which makes the conclusions of the ablation study less convincing.
3．The novelty is limited. The pretraining process of UniQA merely applies a standard contrastive learning strategy, a commonly used approach in numerous previous works. Additionally, the Multi-Cue Integration Adapter directly adopts the inference approach of CLIP-IQA, while the visual feature adaptation module functions similarly to a LoRA operation for fine-tuning. This Adapter appears unrelated to the concept of Multi-Cue Integration.  It is not clear how the proposed approach improves upon or differs from standard contrastive learning and existing methods like CLIP-IQA. And  a more detailed explanation of how the Adapter relates to the concept of Multi-Cue Integration is suggested.
4．This paper dedicates extensive sections to describing the dataset construction process but lacks detailed information about the resulting dataset, such as sample size, text length distribution, or word cloud distribution. There is also an absence of detailed statistical analysis and comparison between the IQA and IAA datasets used for training, leaving the relationships and differences between them unexplored.
5. The paper contains minor errors that need careful review. For instance, in Line 365, "supplementary material" should be referred to as the "Appendix."

[1] Wu H, Zhang Z, Zhang W, et al. Q-align: Teaching lmms for visual scoring via discrete text-defined levels. ICML 2024

[2] Shi T, Chen C, Wu Z, et al. Improving Image Aesthetic Assessment via Multiple Image Joint Learning. ACM Transactions on Multimedia Computing, Communications and Applications, 2024.

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confidence: 3
contribution: 2
initial_rating: 5
presentation: 2
questions: Could you demonstrate the effectiveness of your design in generating high-quality text and apply UniQA to a broader range of downstream tasks ?
soundness: 2
strengths: 1) This paper is well-written. 
2) This paper achieves the SOTA performance on IQA and IAA tasks
summary: This paper has two main contributions
1) This paper constructs a high-quality image-text dataset about image quality and aesthetics. 
2) This paper proposes UniQA to effectively learn a general perception of image assessment.
weaknesses: The overall framework and concept are simple and lack novelty.
1) Directly utilizing MLLMs to generate text is now common practice.
2) The pre-training design is straightforward and typical of MLLMs.
3) This paper reports only the performance on IQA and IAA, offering a limited range of downstream tasks.

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confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: I am curious whether the combination of IQA and IAA pipelines makes sense. In the ablation experiment, using only IQA does not seem to be too bad. Therefore, I am not sure whether it is necessary to use a dedicated IAA pipeline. I think the quality in IQA is not purely low-level quality, but also includes aesthetic elements, so removing IAA will not lead to a significant drop in performance. Note that this is not to question weakness, just kindly discuss with the author: Is IAA a subtask of IQA, or an equally important task?
soundness: 3
strengths: The refine module proposed in this paper can transform messy data into a quality-related format, thereby ensuring that the model is highly consistent with the supervised experience of the human eye, and has the potential to be applied in future IQA and IAA tasks. This can achieve the migration of large-scale, general descriptive datasets to quality-related specialized datasets, which can promote progress in the field of IQA.

The method proposed in this paper can be used for both traditional visual content and emerging AIGC quality assessment. It can promote the further application of AIGC, especially the aesthetic aspects of AIGC.

The experimental dataset is relatively sufficient, and the comparison method is complete and advanced.
summary: This paper designs a novel UniQA framework to perform IQA tasks on different types of visual content. Contains two key designs: (1) Utilize MLLM to generate high-quality text descriptions; (2) Use the text generated for IAA as metadata to purify noisy IAA data. Experiments show that this method achieves state-of-the-art performance on both IQA and IAA tasks, achieving SRCC/PLCC above 0.9.
weaknesses: The core implementation of UniQA is to predict the probability of The image quality is {bad, poor, fair, good, perfect} and then fuse them. This is not a completely new paradigm. As far as I know, this first appeared in Q-Align. However, the author only reviewed this article without comparing them in experiments. Considering the similarities between the two, it is necessary for the author to conduct comparative experiments in Table 1 and emphasize the differences between the two.

It is a good point that the UniQA proposed by the author has a significant advantage in evaluating AIGC. However, the verification using AGIQA-1K/3K is slightly outdated. T2I generation models are developing very rapidly, and even the best 3K images (June 2023) are only of average quality compared to the current AIGC. Therefore, the author can verify the performance of UniQA on the AIGIQA-20K (April 2024) database to better prove its applicability to AIGC.

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confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: See Weakness
soundness: 3
strengths: 1. UniQA effectively combines IQA and IAA tasks, extracting shared representations, leading to efficient and comprehensive visual assessment capabilities.

2. Using MLLMs to generate high-quality text descriptions enriches the dataset.

3. The lightweight Multi-Cue Integration Adapter allows UniQA to adapt efficiently to various downstream IQA and IAA tasks with minimal parameter adjustments.
summary: This paper introduces a unified model for assessing both image quality and aesthetics, called UniQA. It uses multimodal large language models (MLLMs) to generate descriptive text for IQA and IAA datasets, which allows for a more detailed and refined training dataset. The model uses these captions for pre-training and employs a lightweight Multi-Cue Integration Adapter to adapt the model for downstream tasks. Experimental results show UniQA achieving state-of-the-art (SOTA) performance across multiple datasets.
weaknesses: 1. Although this paper effectively utilizes MLLM-generated text for dataset construction, the generated descriptions tend to have similar structures and expressions, resulting in limited text diversity. The model's performance heavily depends on the quality of MLLM-generated text, which may introduce noise or bias, especially as MLLMs may produce overly positive or vague evaluations when generating image descriptions.

2. While this method aligns IQA and IAA datasets by unifying them on a common MOS scale to reduce MOS biases between datasets, such direct alignment may overlook the inherent rating standards and subtle differences across datasets. For instance, different datasets may prioritize specific visual features (e.g., sharpness, color balance) in quality assessment, while aesthetic assessment might focus more on composition and emotional impact. In such cases, a unified MOS scale may reduce the model's sensitivity to certain features, potentially compromising its performance precision in specific tasks.

3. The effectiveness of the dataset constructed in this paper largely depends on MLLM-generated text descriptions and data purification strategies. However, it lacks a systematic evaluation of dataset quality. While the data purification process uses Aesthetics-relevance Rank (AR) and Informativeness Rank (IR) to filter out irrelevant content, these subjective criteria may filter out some valuable information, which could affect the dataset's diversity and comprehensiveness.

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confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: - How the MOS-based text guidance G is obtained? Have the authors try several other attempts?
- What exactly does multi-cue mean in the multi-cue integration adapter.
- How much will performance improve if replace the backbone CLIP-B/16 to other MLLMs such as LLaVA or more latest models? If it improves then why not use it?
soundness: 3
strengths: - Effective data generation and refinement using MLLMs and efficient adaptation to each benchmark. 
- Tackles IQA and IAA at the same time.
- Comparisons with a number of previous methods.
summary: This paper introduces a unified vision-language pre-training of quality and aesthetics (UniQA) to tackle both the image quality assessment (IQA) and the image aesthetic assessment (IAA) tasks.
The proposed method first generates quality- and aesthetics-related descriptions by using multimodal large language model (MLLMs) and use them to refine authentic noisy data.
Then the UniQA model is pre-trained with the purified data and finally a lightweight adapter is adapted to each IQA and IAA benchmark.
In the pre-training, two tasks are bridged and the UniQA can learn rich correlated information to enhance the both tasks.
weaknesses: - In obtaining IR score, the informativeness of text is measured by the sentence length but it seems sub-optimal and less robust.
- It is not verified that the quality-level keywords, "bad, poor, fair, good, perfect", are reasonable without any intuitions or experiments. In addition, they are linked to score values (fig 3 b) "0.2, 0.4, 0.6, 0.8, 1.0", respectively, but it is an arbitrary matching. Likewise, the reason using a prompt ensemble with keywords "extremely blurry, blurry, fair, sharp, extremely sharp" have not been verified.
- The results of the prompt ensemble, which gives a major improvement, are not reported in table 1-3.
- It is unclear how can the UniQA handle real-world images.

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confidence: 5
contribution: 2
initial_rating: 5
presentation: 2
questions: 1.The meaning of "versatile cues" in Line 99 is unclear. Where in the Methods section is this concept demonstrated? Additionally, what exactly does "Multi-Cue" refer to in Line 111?

2.What is the motivation for using sentence length as a score to represent information quantity in Lines 254–255?
soundness: 2
strengths: 1.This paper includes extensive visualization experiments that provide a clear and intuitive demonstration of the experimental results of the proposed method.

2.Addressing the current scarcity of high-quality text-image datasets in IAA and the low quality of text descriptions, this study uses MLLM for data cleaning, constructing a higher-quality text-image IAA dataset to support future training.
summary: To learn mutually beneficial representations shared by both tasks explicitly, this paper proposes Unified vision-language pre-training of Quality and Aesthetics (UniQA), which can extract useful and common representations from two tasks. Specifically, an image-text dataset about image quality and aesthetics is constructed by MLLMs, which is used to train the UniQA based on contrastive learning. Then a lightweight adapter is used to fine-tune the specific dataset of two tasks.
weaknesses: 1.Although the premise of this paper is intriguing (aiming to address both IQA and IAA problems simultaneously), similar approaches have already been proposed and with better outcomes. For example, [1] tackles IQA and IAA as well as VQA, surpassing this paper in scope and effectiveness. Additionally, this paper lacks a comparison with [1], making its experimental results less convincing.

2.The proposed method lacks substantial innovation. Overall, UniQA uses CLIP and a LoRA-like fine-tuning approach, with minimal improvements in training paradigms or network architecture.

3.Due to limited innovation in the training method, this work resembles more of a dataset paper, as its main contribution is the MLLM-based text-image IQA and IAA dataset. While the authors dedicate considerable detail to the dataset construction process, they fail to provide specifics on dataset content (e.g., image sources, dataset size, data distribution).

4.The writing structure is somewhat disorganized. For instance, in the introduction, the authors first introduce their UniQA method, then critique existing work, and return to explaining their method, which disrupts the flow. In the related work section, each subsection is extremely brief; more comprehensive discussion of recent work and analysis of similarities and distinctions between this method and others are needed. Overall, the writing structure should be refined to improve readability.

5.The experiments are insufficiently comprehensive. The comparison covers only a single 2024 method, which is far from adequate. As far as I know, several new methods were introduced in 2024, such as [1], [2], [3], [4], [5], etc. Additionally, it would be beneficial if Table 1 reported variance for the experimental results.

[1] Wu H, Zhang Z, Zhang W, et al. Q-align: Teaching lmms for visual scoring via discrete text-defined levels[J]. arXiv preprint arXiv:2312.17090, 2023.

[2] Zhong Y, Wu X, Zhang L, et al. Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference[C]//Forty-first International Conference on Machine Learning.2024

[3] Avanaki N J, Ghildiyal A, Barman N, et al. LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model[J]. arXiv preprint arXiv:2408.17057, 2024.

[4] Yu Z, Guan F, Lu Y, et al. Sf-iqa: Quality and similarity integration for ai generated image quality assessment[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 6692-6701.

[5] Fu H, Wang Y, Yang W, et al. DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild[J]. arXiv preprint arXiv:2405.19996, 2024.

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confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
questions: 1. Line41-42: “high-quality images tend to possess a higher aesthetic appeal compared to their low-quality counterparts.” This conclusion may not true since an aesthetically pleasing old photo may have some noise in it. Besides, some AI-generated images are over smooth which may exhibits high technical quality but with low aesthetic.
2. Is there any word limit in generating captions for images? Since the accuracy of description largely affect the performance. 
3. What is the version of Qwen in Tab. 6 (Ablation on different MLLMs). Please include more information of parameter quantity. In addition, currently, the evaluated MLLMs are all open-source models, the performance difference between them is not significant, it is necessary to evaluate on proprietary MLLMs, such as GPT-4o, Gemini 1.5 Pro, and Qwen-VL-MAX, which owns better visual perception abilities. Experiments on Whether better description can enhance the performance is valuable.
4. When two images (for example, the right two good images of CLIVE in Fig. 4) have similar technical quality and semantic, the generated caption could be very close. In this case, the MOS-based text guidance may not reflect their true quality or aesthetics differences. 
5. In line210-212: The author divide images into 5 levels based on MOS to obtain G, If an image’s MOS ranks in the top 20% of the score range, its level is assigned to perfect. The question is whether there is some loss in performance by quantifying the otherwise continuous scale scores back to a 5-level evaluation criterion, since the MOS distribution in the IQA dataset is almost inhomogeneous, sometimes showing left-skewed or right-skewed. In other words, does the granularity of the evaluation scale affect the model performance?
6. Another question is that the MOS of an image is collected from human study. The quality- and aesthetics-related captioning is completed by MLLMs. Whether the MLLMs can perceive as human remains an problem especially in some aesthetics judgments.
7. Using the length of a sentence as informativeness score may introduce large bias since it may contain some useless words. Why don’t you use Information relevance metrics to measure the informativeness of text?
8. It seems that the proposed method performs well on mainstream IQA datasets. The author further evaluate the generalization capability on AGIQA-3K. Since the naturalness problem [1] (consists of both technical quality and rationality (images which own similar technical quality with NSIs but could contain irrational contents) distortions) appears more easier in AI-generated images than pure quality problem. Is it possible to test on the AGIN dataset [1]? This may demonstrate a significant benefit of this method in the era of generative AI.

[1] Chen, Z., Sun, W., Wu, H., Zhang, Z., Jia, J., Ji, Z., ... & Zhang, W. (2023). Exploring the naturalness of ai-generated images. *arXiv preprint arXiv:2312.05476*.
soundness: 3
strengths: 1. The idea of developing a foundational model with robust visual assessment perceptions consistent with human to benefit both IQA and IAA tasks is novel.
2. The idea of using text descriptions as a bridge to integrate the two tasks is innovative
3. The paper provides a thorough evaluation of existing IQA and IAA models on respective IQA and IAA datasets.
summary: This paper propose a Unified vision-language pre-training for Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) tasks, which breaks down the barrier between quality-, and aesthetic-related features.  The authors construct a high-quality image-text dataset about image quality and aesthetics. Using their collected data, they develop UniQA, which learns a general perception of image assessment. Experiments show that the proposed method achieves SOTA performance across multiple IQA and IAA datasets.
weaknesses: 1. The coarse image retrieval results in Fig. 4 may not sufficient. Adding the corresponding MOSs can increase its readability.
2. The assessment appear to be thorough and sound. However, the paper does not have any p-values or confidence intervals to support their comparisons of methods (especially for Tab. 1, 5, and 6).
3. The performance gain compared to other MOS regression-based models is limited, but introduce more efforts in collecting textual description, which poses challenge for the subjective study. I think this problem should be discussed further.