Enhancing Descriptive Image Quality Assessment with a Large-scale Multi-modal Dataset

## Abstract

Abstract With the rapid advancement of Vision Language Models (VLMs), VLM-based Image Quality Assessment (IQA) seeks to describe image quality linguistically to align with human expression and capture the multifaceted nature of IQA tasks.
However, current methods are still far from practical usage.
First, prior works focus narrowly on specific sub-tasks or settings, which do not align with diverse real-world applications.
Second, their performance is sub-optimal due to limitations in dataset coverage, scale, and quality.
To overcome these challenges, we introduce Depict ed image Q uality A ssessment in the Wild (DepictQA-Wild).
Our method includes a multi-functional IQA task paradigm that encompasses both assessment and comparison tasks, brief and detailed responses, full-reference and non-reference scenarios.
We introduce a ground-truth-informed dataset construction approach to enhance data quality, and scale up the dataset to 495K under the brief-detail joint framework.
Consequently, we construct a comprehensive, large-scale, and high-quality dataset, named DQ-495K.
We also retain image resolution during training to better handle resolution-related quality issues, and estimate a confidence score that is helpful to filter out low-quality responses.
Experimental results demonstrate that DepictQA-Wild significantly outperforms traditional score-based methods, prior VLM-based IQA models, and proprietary GPT-4V in distortion identification, instant rating, and reasoning tasks.
Our advantages are further confirmed by real-world applications including assessing the web-downloaded images and ranking model-processed images.
Datasets and codes will be released in our project page . † † † Corresponding Author. † † Project Page: https://depictqa.github.io/depictqa-wild/ .

## 1 Introduction

Image Quality Assessment (IQA) aims to measure and compare the quality of images, expecting to align with human perception.
With the emergence of Vision Language Models (VLMs) [, , ], VLM-based IQA begins to attract more research interest [, , , , ].
These methods leverage VLMs to describe image quality using language, recognizing that language better mirrors human expression, and captures the multifaceted nature of IQA tasks [].
However, existing methods are still far from VLM-based IQA in the wild, especially in their functionality and performance.

Functionality.
There are various application scenarios of IQA, but existing VLM-based IQA models only support a few of them.
For example, one scenario involves assessing a single image downloaded from the web, while another requires comparing multiple images handled by different algorithms.
Also, image restoration needs to assess an image against a reference, while image generation requests non-reference assessments.
Therefore, an in-the-wild IQA model should be multi-functional to cater to such diverse scenarios.
However, existing methods limit to a specific subset of these tasks, such as single-image assessment [], multi-image comparison [], or full-reference setting [], etc. Hence, the limitations in functionality hinder prior methods to be an IQA method in the wild.

Performance.
Many IQA methods perform well on some specific datasets but may generalize poorly to other images with different contents or distortions.
For instance, Co-Instruct [] performs well on TID2013 [] (85.0%), but drops significantly to 50.7% when testing on BAPPS [].
A more comprehensive comparison on our newly created benchmark is given in Fig. 1, where it shows that previous works [, ] under-perform even within their defined tasks and settings.
One potential cause for this is the limited scope of their training datasets.
For example, the added distortion category in Q-Instruct [] is limited; Co-Instruct [] directly utilizes GPT-4V [], which is not accurate in IQA tasks, to generate data; and the dataset scale in DepictQA [] remains small.
Additionally, these methods are constrained in their usage by resizing images to a fixed resolution [, ], while the image resolution is critical for quality assessment.
Therefore, the dataset’s coverage, quality, and scale together with the training techniques limit the performance of previous methods.

Figure: Figure 1: Performance comparison. Our model surpasses previous works including Q-Instruct [], Co-Instruct [], and the proprietary GPT-4V [] across a broad range of tasks in both full-reference and non-reference settings. Traditional score-based IQA methods like LPIPS [] and MUSIQ [] have no language abilities, and thus can only be used in instant rating task.
Refer to caption: x1.png

To overcome these challenges, we build an in-the-wild, multi-functional IQA model.
We group multi-functional IQA tasks into two types, as shown in Fig. 2.
(a) Single-image assessment aims to evaluate the quality of a single image by identifying distortions (e.g., “blur” in Fig. 2a top).
It can also analyze the distortions’ impacts on contents (e.g., blur “affecting the definition of mountains and trees” in Fig. 2a bottom).
(b) Paired-image comparison focuses on comparing the quality of two distorted images based on the clarity, colorfulness, and sharpness of presented contents.
For example, in Fig. 2b, despite reduced contrast, “Image A maintains more scene integrity”, as “Image B’s serious noise level is more detrimental”.
We omit multi-image comparison since it is an easy extension of a pairwise one [].
Each task comprises a brief sub-task focusing on fundamental abilities, and a detailed sub-task fostering reasoning capacities.
Furthermore, all tasks accommodate both full-reference and non-reference settings.
These designs cater to diverse application scenarios.

Under the multi-functional task paradigm, we construct a new large-scale dataset, DQ-495K, for comprehensive and accurate training and evaluation.
First, for diverse distortion, we design and implement 35 types of distortions, each with 5 levels.
Second, to enhance the label quality, we inform GPT-4V of the low-level ground truths (e.g., distortion information) to leverage its strong high-level perception and language abilities, while avoiding its relatively sub-optimal IQA capabilities.
Third, to increase the dataset scale, we scale up the data amount to 495K under the brief-detail combined framework [].
Moreover, our dataset is suitable for both full-reference and non-reference settings.

With DQ-495K dataset, we then train a VLM model, named Depicted image Quality Assessment in the Wild (DepictQA-Wild).
During training, the original image resolution is retained, leading to a better quality perception regarding resolution.
Furthermore, we estimate the confidence of responses from key tokens, providing vital auxiliary information, especially for filtering low-quality responses.

The performance of DepictQA-Wild is evaluated in Fig. 1 and Sec. 5.
In brief tasks, our model surpasses general VLMs, IQA-specific VLMs, and score-based IQA methods by a large margin.
For example, we achieve 95.9% in non-reference comparison on TID2013, remarkably surpassing Co-Instruct (85.0%) and GPT-4V (88.1%).
In detailed tasks, our model also excels, e.g., recording 74.9% in non-reference comparison reasoning, compared to 48.1% for Co-Instruct and 60.3% for GPT-4V.
At last, experiments on real-world applications including assessing web-downloaded images and comparing model-restored images further demonstrate our superiority.
We hope that our multi-functional model could serve as a stepping stone towards a unified VLM-based IQA model. Although not yet fully realized, our method showcases the potential of VLM-based IQA models.

Figure: Figure 2: Task paradigm and qualitative results. DepictQA-Wild focuses on two tasks including single-image assessment and paired-image comparison in both full-reference and non-reference settings. Each task contains a brief sub-task focusing on the fundamental IQA ability, and a detailed sub-task fostering the reasoning capacities. The shown results are tested in the non-reference setting.
Refer to caption: x2.png

## 2 Related Works

Score-based IQA methods.
Traditional IQA methods rely on scores to assess image quality and can be divided into full-reference and non-reference methods.
(a) Full-reference methods compute a similarity score between a distorted image and a high-quality reference.
Early works rely on human-designed metrics such as image information [], structural similarity [], phase congruency with gradient magnitude [], etc. The rapid advancement of deep learning has also inspired learning-based IQA methods that measure image quality through data-driven training.
Pioneered by PieAPP [] and LPIPS [], data-driven approaches [, , , , , , ] have spurred innovations in IQA, exhibiting high consistency with human judgments.
(b) Non-reference methods directly regress a quality score without a reference image.
Initially, human-designed natural image statistics are adopted [, , , , , , ].
Subsequently, deep-learning-based methods [, , , , , , ] replace hand-crafted statistics by learning quality priors from extensive data.
Recent works focus on enhancing performance by introducing multi-scale features [], CLIP pre-training [], multi-dimension attention [], continual learning [], multitask learning [], and so on.
However, as discussed in [], score-based IQA methods limit themselves in complex analyses and multi-aspect weighing of IQA, since the information provided by a single score is far from sufficient.

Vision Language Models (VLMs) incorporate visual modality into large language models [, , ], aiming to leverage their emergent ability to achieve general visual ability.
These VLMs [, , , , , , , , , ] have demonstrated a general visual ability and can tackle a variety of multi-modality tasks, including image captioning [, , ], visual question answering [, , , ], document understanding [, , ], etc. Although proficient in these high-level perception tasks, we demonstrate in Sec. 5 that general-purpose VLMs still struggle with IQA tasks.

VLM-based IQA methods aim to achieve better alignment with human perception leveraging the power of VLMs [].
Q-Bench [, ] establishes a comprehensive benchmark for evaluating general-purpose VLMs in low-level perception tasks.
[] evaluates various VLMs on the widely-adopted two-alternative forced choice (2AFC) task.
Q-Instruct [] enhances the low-level perception ability of VLMs by introducing a large-scale dataset.
Q-Align [] employs discrete text-defined levels for more accurate quality score regression.
Co-Instruct [] concentrates on the quality comparison among multiple images.
DepictQA [] performs quality description, quality comparison, and comparison reasoning in the full-reference setting.
Nonetheless, as highlighted in Sec. 1, these methods focus only on specific aspects of IQA tasks, diverging from the original intents of VLMs’ universality and practical usage requirements, and their performance remains sub-optimal.

## 3 Task Paradigm and Dataset Construction

### 3.1 Task Paradigm

As highlighted in the introduction, there are various application scenarios for IQA models.
First, the evaluation objective can be either single-image assessment or paired-image comparison. The former is useful to rate a web-downloaded image, while the latter suits comparing images processed by two different algorithms.
Second, the reference setting may be full-reference or non-reference. For example, image restoration requires assessments based on references, while image generation needs non-reference evaluations.
Third, the response could be either brief or detailed. Brief responses suit well-targeted tasks (e.g., comparison without reasons), while detailed responses enhance interpretability and human interaction.
To cater to such diverse scenarios, an in-the-wild IQA method should be multi-functional.
Therefore, we aim to establish such a multi-functional task paradigm for VLM-based IQA research.
As shown in Fig. 2, we focus on two tasks, each containing both brief and detailed sub-tasks, and supporting both full-reference and non-reference settings.

- •
Task1: single-image assessment.
(a) Brief sub-task: distortion identification.
Given a distorted image, the model should identify the most obvious distortions.
(b) Detailed sub-task: assessment reasoning.
In addition to identifying distortions, the model should also describe how these distortions affect the perception of image contents and the overall image quality.
- •
Task2: paired-image comparison.
(a) Brief sub-task: instant rating.
Given two distorted images, the model should find the image with better quality.
(b) Detailed sub-task: comparison reasoning.
Building upon the comparison results, the model should first compare the content loss caused by distortions in the two images, then weigh different aspects to draw inferences, and finally justify its comparison results.
Note that we omit the multi-image ($>$2) comparison since it can be achieved easily as the extension of paired case [].

Compared with previous works, our design unifies various tasks, response types, and reference settings into a multi-functional paradigm.
In contrast, Q-Instruct [] focuses on non-reference single-image assessment, Co-Instruct [] targets comparison among multiple images in the non-reference setting, and DepictQA [] primarily addresses the full-reference setting.
Although one can achieve unified IQA by combining these task-specific IQA models, it is impractical due to the significant increase in network parameters, considering that current VLMs are already quite large.

### 3.2 Distortion Library

**Table 1: Overview of our distortion library with 12 super-categories and 35 sub-categories in total.**
| Super-category | Blur | Noise | Compression | Brighten | Darken | Contrast<br>Strengthen | Contrast<br>Weaken | Saturate<br>Strengthen | Saturate<br>Weaken | Over-sharpen | Pixelate | Quantize |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| # Sub-category | 6 | 6 | 2 | 4 | 4 | 2 | 2 | 2 | 2 | 1 | 1 | 3 |

Existing IQA datasets (e.g., BAPPS [], PieAPP []) usually introduce distortions (e.g., noise, blur) into high-quality reference images to create distorted images for evaluation.
However, these datasets do not publicly release the distortion information of each image, and their distortions only cover limited scenes.
Therefore, we aim to develop a comprehensive large-scale distortion library.

Distortion generation.
Our distortion system comprises 12 super-categories in total, with each super-category consisting of multiple sub-categories.
For instance, the “blur” category encompasses “Gaussian blur”, “motion blur”, “lens blur”, etc. In total, there are 35 sub-categories.
For each sub-category, there are 5 severity levels: “slight”, “moderate”, “obvious”, “serious”, and “catastrophic”.
A summary is illustrated in Tab. 1.
Considering the need to assess high-quality images as well, we retain the original image without any distortions in 5% proportion.
See details in Appendix.

Multi-distortion setups.
In practical usage, multiple distortions may occur simultaneously on the same image.
While a simple way to simulate them is to add multiple distortions recursively, real-world scenarios are more complex.
First, one distortion may weaken another, such as “brighten” weakens “darken”, “blur” weakens “over-sharpen”.
Second, certain distortions exhibit similar visual results, such as “pixelate” looks similar to “blur”, making it challenging to identify both if they are applied simultaneously.
We also observe that humans can identify at most two distortions when three or more are applied.
Hence, we limit the distortion number to two and manually review all possible combinations to exclude contradictory or similar combinations.
See details in Appendix.

### 3.3 Dataset Construction

High-quality and large-scale datasets are crucial for training VLMs.
Following [, ], training VLMs requires {images, question, response} triplets, where “images” are the ones to be evaluated, “question” describes the task, and “response” is the ground truth answer.
In this section, we detail the construction of our dataset from the selection of images and the collection of questions and responses.

Image collection.
Typical IQA datasets involve two types of images: high-quality reference images and distorted images to be evaluated.
Generating distorted images is easy given our comprehensive distortion library introduced in Sec. 3.2.
Existing studies often collect a large number of distorted images from a small number of references [, ].
However, the semantic richness of images is also crucial for VLM training.
Therefore, we primarily source reference images from the KADIS-700K dataset [], which offers 140K pristine reference images from diverse natural and daily scenes.
We also leverage other IQA datasets for their convenience to generate responses (details in the following).

Question collection.
Humans often express similar questions using different sentences, necessitating model robustness to various user questions.
For each task, we initially prompt GPT-4 [] to generate 50 candidate questions.
Subsequently, we manually eliminate ambiguous and repetitive ones and correct inaccurate ones, creating a question set of 20 questions (see Appendix).
These questions are randomly sampled during training and testing to form the data pair.

Response collection.
We employ two response types as shown in Fig. 3.
The first comprises brief templated responses that are easy to produce, where we emphasize the quantity to bring robust fundamental skills.
The second consists of detailed responses, where we emphasize the quality to enhance the model’s advanced reasoning abilities.
Existing methods to collect detailed responses mainly rely on human annotation [, ] and GPT-4V generation [].
However, human annotation can be biased and vary in quality particularly when annotators are untrained or tired [].
Also, GPT-4V is not fully reliable since its IQA performance is still unsatisfactory as evidenced in Sec. 5.

We rethink the key aspects of our desired responses and GPT-4V’s corresponding abilities, introducing GT-informed generation by prompting the Ground Truth (GT) details to enhance GPT-4V’s generation.
Specifically, a high-quality detailed response should contain image contents, key distortions, the impacts of distortions on contents, and conclusions (e.g., comparison results).
While GPT-4V excels at identifying contents and analyzing impacts, it struggles with distortion identification and quality comparison, which will be shown in Sec. 5.
To compensate for that, we directly provide it with explicit GT information.
The response generation for each task is detailed subsequently.

Figure: Figure 3: Construction of our DQ-495K dataset. For distortion identification, templated responses are generated using distortion information. In instant rating, we sample images from existing datasets and compare the Mean Opinion Score (MOS) to determine the better image for templated response creation. For assessment reasoning and comparison reasoning tasks, we provide GPT-4V with evaluated images and Ground Truth (GT) details (i.e., distortion information, comparison results from an assistant model) to facilitate detailed and accurate response generation, called GT-informed generation. This additional information is critical as GPT-4V cannot produce it accurately.
Refer to caption: x3.png

Task1-brief: distortion identification.
As shown in Fig. 3a, we first establish a response pool containing 20 templates with unspecified distortions.
Next, we add distortions into the reference to create its distorted counterpart and populate a sampled template with the specific distortions to complete the response.
For streamlined evaluation, inspired by [], we randomly select half of the questions and append the short answer prompt: “Answer the question using a single word or phrase.”
Correspondingly, the response will be a single phrase, like “noise”, specifying the distortions.

Task1-detail: assessment reasoning.
Given the reference image, we initially introduce distortions to corrupt the reference.
Then, GPT-4V is input with both two images and the distortion information, and requested to assess the quality of the distorted image, as illustrated in Fig. 3b.
We instruct GPT-4V to respond from three dimensions: contents, distortions along with their impacts on contents, and overall quality.
Here prior studies [, ] primarily focus on low-level properties, while we consider how these low-level distortions influence the display of high-level contents.

Task2-brief: instant rating.
We begin by sampling a reference image and its two distorted versions from existing IQA datasets, and then compare the Mean Opinion Score (MOS) to determine the better one, as shown in Fig. 3c.
Similar to distortion identification, we assemble a response pool of 20 templates to convert the comparison results into textural responses, and append the short answer prompt for the convenience of evaluation.
We select three IQA datasets for training, including BAPPS [], KADID-10K [], and PIPAL [], to cover a diverse range of reference images.

Task2-detail: comparison reasoning.
As depicted in Fig. 3d, given a high-quality image, we randomly apply distortions to produce two distorted images.
We first train an assistant model using the large-scale instant rating data to predict the comparison results.
Note that GPT-4V does not perform well on the quality comparison task, as shown in our experiments in Tab. 4 and Tab. 7, and thus we train our own comparison model.
Then, similar to assessment reasoning, we inform GPT-4V of the three images, distortion information, and comparison results to generate detailed responses.

Setup of non-reference setting.
Our dataset accommodates both full-reference and non-reference settings.
However, even for humans, identifying subtle distortions (e.g., minor brightness adjustments) without a reference is challenging.
Thus, in the non-reference setting, we selectively remove samples with “slight” severity on some specific distortions, including “brighten”, “darken”, “contrast weaken”, “contrast strengthen”, “saturate weaken”, “saturate strengthen”, “quantize”, and “over-sharpen”.

**Table 2: Statistics of our DQ-495K dataset.**
|  | Task1-brief | Task1-detail | Task2-brief | Task2-detail |
| --- | --- | --- | --- | --- |
|  | Distortion<br>Identification | Assessment<br>Reasoning | Instant<br>Rating | Comparison<br>Reasoning |
| Train | 112,000 / 112,000 | 19,829 / 7,981 | 215,676 | 19,809 / 7,958 |
| Validation | 28,000 / 28,000 | 200 / 100 | 41,120 | 200 / 100 |

Dataset statistics.
The dataset statistics are illustrated in Tab. 2 (more in Appendix).
All tasks, except instant rating, are displayed in the single-distortion / multi-distortion format.
Our training set contains 439,676 brief samples and 55,577 detailed samples.
For instant rating, the training set includes BAPPS, KADID, and PIPAL, while the validation set consists of BAPPS, KADID, PIPAL, TID2013 [], LIVE-MD [], and MDID2013 [].
To ensure no intersection between training and validation sets for those overlapped datasets, the original splits are kept.
For detailed tasks, all samples in the validation set have been carefully checked by humans.

## 4 Model Design

We primarily follow DepictQA [] to construct and train our model.
The base large language model is Vicuna-v1.5-7B [].
Following [], we also adopt the unique tag technique, i.e., using specific tags for various types of images, to ensure that the language model can distinguish different input images.
To increase robustness, an external high-level perception dataset (detailed description dataset in []) is incorporated during training as a regularization, similar to [, ].
See details in Appendix.

Retaining resolution in training.
Although previous VLM-based IQA models typically resize all input images to a fixed resolution [, ], we find this might hurt their performance, as resolution variation may affect visual quality.
Instead, we retain the original image resolution during training.
Specifically, we interpolate (in bicubic mode) the position embedding in CLIP [] to accommodate varying image resolutions.
Ablation studies detailed in Sec. 5.4 demonstrate our model’s capability to assess quality variations attributable to resolution, even without explicitly training on such tasks.

Confidence estimation.
In many applications, it is important to know a confidence score that indicates when the model is uncertain of its response.
Here we use the confidence scores of some key tokens as the confidence of the entire answer.
Intuitively, the key tokens are distortion names in distortion identification, and are either “Image A” or “Image B” in instant rating.
For detailed reasoning tasks, which feature diverse and non-structured responses, we utilize semantic change testing [] to identify the top 20 tokens with the highest importance scores as key tokens.
In semantic change testing, we employ all-MiniLM-L6-v2 [] as the similarity model, due to its high processing speed (14K sentences per second).
The predicted likelihood of key tokens is averaged as the confidence score.
Fig. 6 verifies that confidence and model performance are highly correlated.

## 5 Experiments

We conduct extensive experiments on benchmarks and real-world applications including assessing web-downloaded images and comparing model-processed images, validating our advantages.

### 5.1 Metrics and Baselines

Accuracy.
The accuracy metric is utilized for distortion identification and instant rating tasks.
VLMs usually produce diverse textual outputs, and we transform them into brief results for accuracy calculation.
Specifically, we prompt our DepictQA-Wild with “Answer the question using a single word or phrase” to encourage direct output of brief responses.
For baseline models, we include all potential answers in the prompt and instruct the model to identify the most accurate one.

GPT-4 score as evaluation metric.
We employ the GPT-4 score to evaluate assessment reasoning and comparison reasoning tasks, following [].
Specifically, we provide GPT-4 with both the model-generated response and the corresponding ground truth response.
GPT-4 assesses the helpfulness, relevance, accuracy, and level of detail in the model-generated response relative to the ground truth, assigning an overall score on a scale of 0 to 10, where a higher score indicates better quality.
This average score is subsequently normalized to a scale of 0 to 100%, reported as the GPT-4 score metric.

Baselines.
We categorize baseline methods into general-purpose VLMs and IQA-specific VLMs.
For general VLMs, we include mPLUG-Owl2 [] (based on LLaMA-2-7B []), LLaVA-1.6 [] (based on Vicuna-v1.5-7B []), and the proprietary GPT-4V [].
IQA-specific VLMs are represented by Q-Instruct [] and Co-Instruct [].
Additionally, we compare traditional score-based IQA methods including full-reference ones (PSNR, SSIM [], LPIPS [], DISTS []) and non-reference ones (NIQE [], ClipIQA [], MUSIQ [], MANIQA []) in instant rating task.

### 5.2 Results on Benchmarks

**Table 3: Distortion identification results under both single-distortion and multi-distortion cases. The accuracy metric is reported in the full-reference / non-reference settings. DepictQA-Wild greatly outperforms all baselines and maintains its high accuracy in out-of-distribution (OOD) setting.**
|  | General VLM | IQA-specific VLM |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | mPLUG-Owl2 [] | LLaVA-1.6 [] | GPT-4V [] | Q-Instruct [] | Co-Instruct [] | DepictQA-Wild | DepictQA-Wild (OOD) |
| Single-dist. | 10.1 / 11.6 | 14.0 / 15.3 | 46.7 / 45.2 | 12.9 / 15.5 | 27.2 / 34.4 | 97.9 / 94.7 | 82.1 / 73.2 |
| Multi-dist. | 10.8 / 10.7 | 12.0 / 12.1 | 41.5 / 39.8 | 21.5 / 23.9 | 30.2 / 33.3 | 90.5 / 89.5 | 76.6 / 77.2 |

Quantitative results of distortion identification are shown in Tab. 3.
First, the performance of Q-Instruct and Co-Instruct is stably superior in the non-reference setting compared to the full-reference setting, attributed to their training without reference.
Second, the performance of open-source general VLMs, including mPLUG-Owl2 and LLaVA-1.6, is still limited, but the proprietary GPT-4V [] outperforms other general-purpose VLMs and exceeds prior specialized IQA VLMs.
Third, DepictQA-Wild significantly surpasses all baseline methods, demonstrating our model’s efficacy.
Finally, we evaluate our model in an out-of-distribution (OOD) setting.
Specifically, for a particular category of distortion (e.g., noise), we use some sub-categories (e.g., Gaussian noise) during training, and different sub-categories (e.g., speckle noise) for evaluation.
Results in the last column of Tab. 3 show that our method maintains high accuracy even under such an OOD setting.

Quantitative results of instant rating are demonstrated in Tab. 4.
First, in the full-reference context, traditional score-based methods, even the simplest PSNR, outperform all general VLMs including GPT-4V and prior IQA-specific VLMs, indicating the inadequacy of existing VLMs in full-reference IQA tasks.
Second, conversely, in the non-reference scenario, GPT-4V and Co-Instruct excel beyond most score-based approaches, except MUSIQ.
Third, Co-Instruct is trained on multi-image comparison tasks without reference, and thus its performance in full-reference setting drops by quite a large margin.
This further demonstrates the necessity of unifying full-reference and non-reference settings.
Finally, DepictQA-Wild demonstrates superior performance across both settings by a large margin, showcasing its substantial advantage.

**Table 4: Instant rating results on multiple benchmarks in the full-reference / non-reference setting with the accuracy metric. DepictQA-Wild surpasses all baselines by a large margin.**
| Methods | BAPPS [] | KADID [] | PIPAL [] | TID2013 [] | LIVE-MD [] | MDID2013 [] | Mean |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Full-refer.<br>Score-based<br>IQA | PSNR | 68.9 / | 78.7 / | 80.9 / | 85.0 / | 89.7 / | 78.0 / | 80.2 / |
| SSIM [] | 69.7 / | 77.1 / | 82.6 / | 78.7 / | 88.1 / | 76.8 / | 78.8 / |  |
| LPIPS [] | 79.4 / | 79.7 / | 84.2 / | 86.6 / | 91.3 / | 85.4 / | 84.4 / |  |
| DISTS [] | 79.7 / | 85.8 / | 84.6 / | 87.0 / | 93.1 / | 88.5 / | 86.5 / |  |
| Non-refer.<br>Score-based<br>IQA | NIQE [] | / 49.9 | / 66.9 | / 59.7 | / 65.0 | / 86.9 | / 82.2 | / 68.4 |
| ClipIQA [] | / 59.7 | / 75.8 | / 72.6 | / 85.8 | / 65.8 | / 47.0 | / 67.8 |  |
| MUSIQ [] | / 59.2 | / 76.1 | / 77.8 | / 80.1 | / 87.2 | / 81.1 | / 76.9 |  |
| MANIQA [] | / 54.9 | / 68.4 | / 79.2 | / 77.3 | / 75.4 | / 63.5 | / 69.8 |  |
| General VLM | mPLUG-Owl2 [] | 50.1 / 50.1 | 50.6 / 50.8 | 49.6 / 49.6 | 48.6 / 48.5 | 49.9 / 50.1 | 50.6 / 50.5 | 49.9 / 49.9 |
| LLaVA-1.6 [] | 54.1 / 56.2 | 50.4 / 51.9 | 52.0 / 52.6 | 54.2 / 57.0 | 54.4 / 56.5 | 54.3 / 53.1 | 53.2 / 54.6 |  |
| GPT-4V [] | 70.3 / 63.2 | 83.2 / 81.5 | 78.5 / 78.2 | 84.4 / 88.1 | 79.6 / 72.7 | 70.6 / 67.6 | 77.8 / 75.2 |  |
| IQA-specific<br>VLM | Q-Instruct [] | 50.4 / 50.1 | 51.8 / 55.8 | 50.4 / 51.9 | 50.8 / 55.0 | 51.8 / 53.0 | 49.6 / 49.6 | 50.8 / 52.6 |
| Co-Instruct [] | 49.8 / 50.7 | 52.0 / 82.4 | 50.6 / 72.5 | 59.3 / 85.0 | 50.0 / 70.3 | 50.0 / 58.0 | 52.0 / 69.8 |  |
| DepictQA-Wild | 82.7 / 81.4 | 92.7 / 92.4 | 89.2 / 88.8 | 96.2 / 95.9 | 92.1 / 91.9 | 89.1 / 88.4 | 90.3 / 89.8 |  |

Quantitative results of assessment reasoning and comparison reasoning are illustrated in Tab. 5.
First, the performance of the two VLM-specific models significantly declines when applied to tasks outside their defined scopes.
For instance, Q-Instruct is not good at comparison tasks and Co-Instruct’s performance is unsatisfactory on full-reference tasks.
Second, GPT-4V shows robust reasoning abilities, stably outperforming prior IQA-specific VLMs.
Third, DepictQA-Wild still surpasses GPT-4V, especially in the non-reference setting, affirming its superior reasoning capabilities.

Figure: Table 5: Assessment reasoning and comparison reasoning results under both single-distortion and multi-distortion cases. GPT-4 score metric is reported in the full-reference / non-reference setting.
Refer to caption: x4.png

Qualitative results of our model on the four tasks in the non-reference setting are depicted in Fig. 2.
More qualitative results are provided in Appendix.

### 5.3 Real-world Applications

Assessing web-downloaded images.
A practical usage of an IQA model involves assessing the quality of real images.
We collect a total of 50 real-world images from the web, featuring diverse contents including animals, plants, faces, buildings, and landscapes.
Qualitative results in Fig. 5 indicate that our method can assess real images with detailed descriptions.
More importantly, DepictQA-Wild can describe how the distortions affect the contents.
For example, in Fig. 5d, our model first accurately identifies the “severe quantization”, then describes that the quantization “causes banding in the sky and water”, and finally concludes that the quality “is considerably degraded”.
We also conduct a user study with 20 participants involved.
Participants are instructed to choose the assessment result that is of the highest quality among the test methods.
The results are shown in Fig. 4, revealing that our approach stably outperforms baseline methods in aligning human perception.

Figure: Figure 5: Qualitative results on assessing web-downloaded images.
Refer to caption: x5.png

**Table 6: Results on model-processed images.**
|  | NIQE | ClipIQA | MUSIQ | ManIQA | GPT-4V | DepictQA-Wild |
| --- | --- | --- | --- | --- | --- | --- |
| Rank $\downarrow$ | 2.20 | 1.40 | 1.60 | 1.80 | 1.34$\pm$0.27 | 1.20 |
| Accuracy $\uparrow$ | 45.5 | 72.7 | 77.3 | 66.4 | 74.5 | 82.7 |

Comparison on model-processed images.
To develop image restoration models, one often needs to compare the restoration quality of different models.
To simulate this, we consider five distortions including “defocus blur”, “motion blur”, “noise”, “JPEG compression”, and “low resolution”.
For each distortion, three to four candidate models are used to process the distorted images.
We manually rank the restored results, assigning “1” to the best restoration, “2” to the second best, etc. Different IQA methods are adopted to compare these restored images pairwise and find the best restoration.
The average rank of the found best restoration and the accuracy of the paired comparison are reported in Tab. 6.
First, DepictQA-Wild achieves an average rank of 1.20 (1 is the best), outperforming both GPT-4V and score-based methods.
Second, though the temperature is set to 0, GPT-4V shows variability with a large standard deviation.
Third, model-restored images are generally out-of-distribution for our model, while DepictQA-Wild exhibits excellent generalization ability on these images.

Figure: Table 7: Our assistant model surpasses GPT-4V greatly in instant rating task. The metric is accuracy in the full-reference / non-reference setting.
Refer to caption: x6.png

### 5.4 Ablation Studies

Assistant model.
To construct comparison reasoning responses, we train an assistant model to predict comparison results (see Fig. 3).
These results serve as pseudo labels, which are subsequently provided to GPT-4V to generate responses.
We compare the assistant model to GPT-4V on three out-of-distribution IQA datasets.
The results in Tab. 7 affirm the superiority of the assistant model.

Retaining resolution.
In Tab. 8, we study the effects of retaining resolution.
We sample 1,000 high-quality images with an aspect ratio greater than $4:3$.
These images are either resized by swapping their height and width (H$\leftrightarrow$W) or down-sampled by a scale factor of 0.8 or 0.9.
The model needs to compare the original and resized images to determine the better one.
The alternative of retaining resolution is resizing the two images to a larger resolution, which can maintain the quality difference between the original and resized images (v.s., resizing to a smaller resolution results in two nearly the same images).
The results in Tab. 8 prove that retaining resolution is crucial for identifying images with better aspect ratio or higher resolution.
More results are provided in Appendix.

Confidence.
We examine the correlation between model performance and estimated confidence in Fig. 6.
For distortion identification and instant rating tasks, across both full-reference and non-reference settings, our model demonstrates improved performance as the confidence interval increases.
These findings validate the effectiveness of our confidence estimation.
More results in Appendix.

## 6 Conclusions and Limitations

We introduce DepictQA-Wild, a VLM-based IQA model, empowered by a new multi-functional task paradigm, dataset enrichment, and training technique, surpassing baseline methods in both benchmarks and two real-world applications, showing the potential of descriptive quality assessment.

Limitations.
First, the fine-grained abilities, which require more high-level perception skills, are still not satisfactory.
For example, in Fig. 5c, though identifying noise and pixelation successfully, our model fails to point out that they are respectively located in the left and right parts.
Second, the task paradigm is not fully unified.
Our comparison tasks focus on images with the same content but different distortions.
Extending this to include comparisons among different contents could enhance the model’s applicability.
Furthermore, besides quality, image aesthetics can also be described linguistically based on VLMs.
Third, whether our assessment can be used as feedback to improve the quality of generation or restoration models is still under-explored.
These belong to our future works.

## Appendix

## Appendix A Overview

This Appendix is structured as follows.
Dataset details are described in Appendix B, followed by the details of model, training and inference in Appendix C.
More ablation studies, qualitative results, and the details of real-world applications are presented in Appendix D.

## Appendix B Dataset Details

### B.1 Details of Distortion Library

Figure: Figure A1: Reference image for all distortion examples shown in Fig. A2.
Refer to caption: extracted/5662701/supp_figs/dist/org.png

As stated in Sec. 3.2, to facilitate the dataset construction, we design and implement a comprehensive distortion library.
Our distortion system contains 12 distortion super-categories in total, with each category consisting of multiple sub-categories.
For instance, the “blur” category encompasses “Gaussian blur”, “motion blur”, “lens blur”, etc. In total, there are 35 sub-categories.
For each sub-category, there are 5 severity levels: “slight”, “moderate”, “obvious”, “serious”, and “catastrophic”.
In this section, we elaborate on our distortion implementations [], including the principles, formulas, and severity setup.We also provide one example for each implementation in Fig. A2, with the reference image in Fig. A1.

Blur.

- •
Gaussian blur. The distorted image is generated by convolving the reference image with a Gaussian blur kernel. We set the kernel size $(s_{k})$ to be a function of the standard deviation $(\sigma_{k})$ of the blur kernel: $s_{k}={\rm round}(4\times\sigma_{k})+1$.
- •
Motion blur. Linear motion blur is applied to the reference image using the linear filter, where $(r,\sigma)\in[(5,3),(10,5),(15,7),(15,9),(20,12)]$.
- •
Glass blur. Filter the image using a Gaussian filter, then randomly jitter each pixel in the image by $x$ pixels, and repeat this process $n$ iterations. $[\sigma,x,n]\in[(0.7,1,1),(0.9,2,1),(1.2,2,2),(1.4,3,2),(1.6,4,2)]$.
- •
Lens blur. This distortion uses the circular average filter, where $r\in[1,2,4,6,8]$.
- •
Zoom blur. The image is gradually zoomed in and overlaid to calculate the average.
- •
Jitter blur. Each pixel is randomly displaced by a shift of ${\rm randint}(-p,p)$ pixels both in $x$ and $y$ dimensions, with a total of 5 displacements, where $p\in[1,2,3,4,5]$.

Noise.

- •
Gaussian noise in RGB space. Additive Gaussian noise is applied to each of the RGB channels of an image, where $\sigma\in[0.05,0.1,0.15,0.2,0.25]$.
- •
Gaussian noise in YCrCb space. Similar to the Gaussian noise in RGB space, this distortion is implemented in YCbCr space, where $(\sigma_{l},\sigma_{r},\sigma_{b})\in[(0.05,1,1),(0.06,1.45,1.45),(0.07,1.9,1.
9),(0.08,2.35,2.35),(0.09,2.8,2.8)]$.
- •
Speckle Noise. Speckle Noise is also known as Multiplicative Gaussian noise, where $\sigma\in[0.14,0.21,0.28,0.35,0.42]$.
- •
Spatially correlated noise. The reference image is first corrupted by an additive Gaussian noise, which results in each pixel being corrupted by an independent and identically distributed noise pattern. The resultant image is then filtered with an average filter of kernel size 3 × 3, correlating the intensity of each pixel with those of the neighboring pixels. More specifically, the distorted image is given by:
$I_{D}(x,y,c)=\frac{1}{|N_{n}|}\sum_{i\in N_{n}}(I_{R}(x_{i},y_{i},c_{i})+N(x_{
i},y_{i},c_{i})),$
(A1)
where $I_{D}$ is the distorted image, $I_{R}$ is the reference image, $N_{n}$ is the set of neighboring pixels, and $N(x,y,c)\sim\mathcal{N}(0,\sigma_{g}^{2})$.
- •
Poisson noise.
This distortion generates Poisson noise based on the image pixel values, where $intervals\in[80,60,40,25,15]$.
- •
Impulse noise. Impulse noise is also known as salt and pepper noise. The density of the noise: $d\in[0.01,0.03,0.05,0.07,0.10]$.

Compression.

- •
JPEG. The distorted image is a JPEG-compressed version of the reference image, where the parameter in Pillow, quality $q\in[25,18,12,8,5]$.
- •
JPEG 2000. This distortion is an advanced compression widely used, where the Pillow’s parameter quality $q\in[29,27.5,26,24.5,23]$.

Brightness.

- •
Brightness shift in HSV space. The RGB image is mapped to HSV, and then we enhance and reduce the brightness by V channel, where $\sigma\in[0.1,0.2,0.3,0.4,0.5]$ for Brightening and $\sigma\in[-0.1,-0.2,-0.3,-0.4,-0.5]$ for darkening.
- •
Brightness shift in RGB space. We enhance and reduce the brightness in all channels, where $\sigma\in[0.1,0.15,0.2,0.27,0.35]$ for Brightening and $\sigma\in[-0.1,-0.15,-0.2,-0.27,-0.35]$ for darkening.
- •
Gamma Brightness tuning in HSV space. The RGB image is mapped to HSV space and then we enhanced and reduce the brightness by V channel with a gamma function, where $\gamma\in[0.7,0.58,0.47,0.36,0.25]$ for brightening and $\gamma\in[1.5,1.8,2.2,2.7,3.5]$ for darkening.

Contrast.

- •
Contrast tuning by scaling. Given an input image $I_{in}$, there is a corresponding $I_{mean}$, which is a gray image in which each element is the mean of $I_{mean}$. The distorted image $I_{D}$ is generated as following:
$I_{D}=I_{mean}*(1.0-\alpha)+I_{in}*\alpha$, where $\alpha\in[0.75,0.6,0.45,0.3,0.2]$ for strengthening and $\alpha\in[1.4,1.7,2.1,2.6,4.0]$ for weakening.
- •
Contrast tuning by stretching. Contrast changing is performed as follows: $I_{D}(x,y,c)=1/(1+(\frac{\bar{I}_{C}}{I_{R}(x,y,c)+\epsilon})\alpha)$, where $I_{D}$ is the distorted image, $I_{R}$ is the reference image, and $\bar{I}_{C}$ is the mean intensity for channel $c$. $\alpha\in[1.0,0.9,0.8,0.6,0.4]$ for weakening, and $\alpha\in[2.0,4.0,6.0,8.0,10.0]$ for strengthening.

Saturate.

- •
Saturate tuning in HSV space. The reference image is firstly mapped into HSV space and then the S channel is scaled, where the scale factor $s\in[0.7,0.55,0.4,0.2,0.0]$ for weakening and $s\in[3.0,6.0,12.0,20.0,64.0]$ for enhancement.
- •
Saturate tuning in YCbCr space. The reference image $I_{R}$ is firstly mapped into YCbCr space and then the distorted image $I_{D}$ is generated like the following formulation:
$I_{D}(x,y,Cb)=128+(I_{R}(x,y,Cb)-128)\times s,$
(A2)
$I_{D}(x,y,Cr)=128+(I_{R}(x,y,Cr)-128)\times s,$
(A3)
where $s\in[0.6,0.4,0.2,0.1,0.0]$ donates the scale factor for weakening and $s\in[2.0,3.0,5.0,8.0,16.0]$ for strengthening.

Over-sharpen.
The reference image $I_{R}$ is firstly processed by a Gaussian blur kernel to generated a blurred image $I_{blur}$.
Then the original image is over-sharpened with ${\rm cv2.addWeighted}(I_{R},1+\alpha,I_{blur},-\alpha,0)$, where $\alpha\in[2,2.8,4,6,8]$.

Pixelate.
The reference image is firstly down-sampled in BOX mode, then up-sampled to the original resolution in NEAREST mode, where the down-sampling factor $\sigma\in[0.5,0.4,0.3,0.25,0.2]$.

Quantize.

- •
Color quantization using histogram equalization. The color elements are divided into an equal histogram for quantization, where the number of classes $c\in[24,16,8,6,4]$.
- •
Color quantization using histogram median. This distortion is implemented by the function PIL.Image.Quantize.MEDIANCUT, where the number of classes $c\in[20,15,10,6,3]$.
- •
Color quantization using OTSU method, which is implemented by existing function skimage.filters.threshold_multiotsu to generate thresholds. The number of classes $c\in[15,11,8,5,3]$.

Figure: Figure A2: Distortion examples of our distortion design. We showcase one example for each distortion implementation. The reference image is depicted in Fig. A1.
Refer to caption: x7.png

Multi-distortion setups.
As discussed in Sec. 3.2, multiple distortions may occur simultaneously on the same image in practical usage.
First, we observe that humans can identify at most two distortions when three or more are applied, thus we limit the number of applied distortions to two.
Second, some distortions could weaken each other’s presentation (e.g., “brighten” weakens “darken”, “blur” weakens “over-sharpen”). Also, certain distortions show similar visual effects (e.g., “pixelate” looks similar to “blur”), making it hard to identify both if applied simultaneously.
Hence, to exclude contradictory or similar distortion combinations, we manually review all possible combinations, and the results in Tab. A1 present all feasible distortion combinations used in our dataset construction.

**Table A1: Multi-distortion setting where we show all feasible distortion combinations.**
| First Distortion | All Possible Second Distortions |
| --- | --- |
| Blur | Brighten, Compression, Contrast Strengthen, Contrast Weaken,<br>Darken, Noise, Quantize, Saturate Strengthen,<br>Saturate Weaken |
| Brighten | Blur, Compression, Noise, Pixelate, Quantize |
| Compression | Blur, Brighten, Contrast Strengthen, Contrast Weaken,<br>Darken, Noise, Saturate Strengthen, Saturate Weaken |
| Contrast Strengthen | Blur, Compression, Noise, Pixelate, Quantize |
| Contrast Weaken | Blur, Compression, Noise, Pixelate, Quantize |
| Darken | Blur, Compression, Noise, Pixelate, Quantize |
| Noise | Blur, Brighten, Compression, Contrast Strengthen, Contrast Weaken, Darken, Over-sharpen, Pixelate, Saturate<br>Strengthen, Saturate Weaken |
| Over-sharpen | Brighten |
| Pixelate | Brighten, Contrast Strengthen, Contrast Weaken, Darken, Noise,<br>Over-sharpen, Quantize, Saturate Strengthen,<br>Saturate Weaken |
| Quantize | Brighten, Contrast Strengthen, Contrast Weaken, Darken, Noise,<br>Over-sharpen, Pixelate, Saturate Strengthen,<br>Saturate Weaken |
| Saturate Strengthen | Blur, Compression, Noise, Over-sharpen, Pixelate, Quantize |
| Saturate Weaken | Blur, Compression, Noise, Over-sharpen, Pixelate, Quantize |

### B.2 Details of Dataset Construction

As described in Sec. 3.3, for brief tasks, the questions and answers are all templated and randomly sampled from a pool.
The questions of detailed reasoning tasks are also sampled from a pool.
The constructed question pools and answer pools (if possible) of distortion identification, instant rating, assessment reasoning, and comparison reasoning tasks are illustrated in Tab. A6, Tab. A8, Tab. A6, and Tab. A8, respectively.

### B.3 Dataset Statistics

Statistics of the response length in our DQ-495K dataset are detailed in Tab. A2.
We provide statistics on both word count and string length.
For the instant rating task, there is no distinction between single-distortion and multi-distortion cases.
We also depict the word length distribution of detailed reasoning responses in Fig. A4.

Wordcloud map of our introduced DQ-495K dataset is given in Fig. A4.
While plotting Fig. A4, we manually exclude “Image A” and “Image B”, since they are constant proper nouns across all texts.
The most frequent words in our DQ-495K dataset (e.g., “overly high”, “color quantization”, “high contrast”, “high saturation”, and “detail”) are all highly relevant to the low-level properties and the visual quality of images.

**Table A2: Response length statistics in our DQ-495K dataset, reported as word count / string length. For instant rating task, there is no distinction between single-distortion and multi-distortion cases.**
|  | Distortion Identification | Assessment Reasoning | Instant Rating | Comparison Reasoning |
| --- | --- | --- | --- | --- |
| Single-distortion | 10.36 / 69.81 | 64.37 / 430.23 | 9.30 / 52.02 | 93.20 / 604.97 |
| Multi-distortion | 12.84 / 88.67 | 87.31 / 588.44 | 114.04 / 740.68 |  |

Figure: Figure A3: Word length distribution of detailed responses in our DQ-495K dataset.
Refer to caption: x8.png

## Appendix C Details of Model Setups

Model Architecture.
DepictQA-Wild primarily adopts the architecture from DepictQA [], structured as follows.
Specifically, the input images and the question texts are first tokenized, then fused, finally processed by the Large Language Model (LLM) for response generation.
(1) Tokenizing input images and question texts.
We use a frozen CLIP pre-trained ViT-L/14 [] as the image encoder to convert the input images into visual tokens.
The question texts are tokenized into textual tokens using the SentencePiece tokenizer [].
To bridge the different embedding spaces of visual and textual tokens, we implement a trainable image projector to map visual tokens into the textual space following [, ].
(2) Token fusion.
We integrate the visual tokens into pre-defined positions within the textual tokens as token fusion.
(3) Response generation using LLM.
The fused tokens are fed into LLM to generate the final response.
Here we mainly conduct experiments with Vicuna-v1.5-7B.
Despite their capabilities, pre-trained LLMs typically do not perform well on IQA tasks without adjustments.
Therefore, we employ LoRA [], a fine-tuning technique that efficiently modifies a small subset of parameters within the LLM.
Specifically, we apply LoRA to adjust the projection layers in all self-attention modules, following [, ].
This approach allows for targeted refinement of the model’s performance on IQA tasks without the need for extensive retraining.

Model setup.
Since the CLIP pre-trained ViT-L/14 [] encodes each $14\times 14$ patch to a visual token, the resolution of the input image should be integer multiples of 14.
Therefore, we first pad the size of input images to integer multiples of 14 with zero-padding.
Although we aim to retain the image resolution, constrained by computation resources, if the image is too large ($>$ 672), we bi-linearly resize the image with its original ratio retained.
We encode the image patches into visual tokens using the CLIP pre-trained ViT-L/14 [], with each token having a channel of 1024.
The image projection layer projects these visual tokens to the hidden dimension of the LLM, which is 4096.
In LoRA, the rank and scale factor are both set as 16.
There are 32 attention layers in the LLM in total.
In each attention layer, the projection weights of “query”, “key”, “value”, and “output” are adjusted using two delta parameters with the shape of $4096\times 16$ and $16\times 4096$, respectively.

Training and inference setup.
In our experiments, we set the LoRA rank to 16.
DepictQA-Wild is trained for 1 epoch with batch size 64.
Adam optimizer with $(\beta_{1},\beta_{2})=(0.9,0.95)$, weight decay 0.001, and learning rate 0.0002 is used for training.
The trainable parameters are 16M, constituting only 0.25% of the total parameters (7B). The model is trained on 8 GPUs (NVIDIA RTX A6000 48G). The training is completed in around 22 hours.
During inference, the temperature is set to 0, since lots of predicted information (e.g., distortion, comparison result) need to be certain.

## Appendix D More Results

Ablation study on retaining resolution.
In Tab. 8 of the main paper, the effects of retaining resolution is studied.
Specially, we randomly sample 1,000 high-quality images whose aspect ratios are greater than $4:3$.
These images are either resized by swapping their height and width (denoted as H$\leftrightarrow$W), or bi-linearly down-sampled by a scale factor of 0.5, 0.75, 0.8, 0.85, 0.9, or 0.95.
DepictQA-Wild is requested to conduct the instant rating task, i.e., compare the original and resized images to determine the superior one.
Note that in Tab. A3, the alternative method of retaining resolution is to resize both original image and resized image to a larger resolution, which can maintain the quality difference.
In contrast, resizing both images to smaller resolution results in two nearly same images.

The results are presented in Tab. A3.
First, overall, retaining resolution is crucial for identifying images with better aspect ratio or higher resolution.
Second, with down-sampling becomes severer (i.e., aspect ratio is from 0.95 to 0.5), the accuracy is improved since the quality drop is more significant.
Third, for severe down-sampling (e.g., aspect ratio is 0.5) where the quality degradation is quite obviously, retaining resolution or just resizing both images to a larger size both perform well ($\leq$ 99.0).
Finally, however, for relatively slight down-sampling (e.g., aspect ratio is from 0.75 to 0.95), the performance of retaining resolution is stably superior than resizing.

**Table A3: Retaining resolution during both training and inference is important to identify images with better aspect ratio or higher resolution.**
| Retain Resolution? | H$\leftrightarrow$W | $0.5\times$ | $0.75\times$ | $0.8\times$ | $0.85\times$ | $0.9\times$ | $0.95\times$ |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Training | Inference |  |  |  |  |  |  |  |
| ✗ | ✗ | 73.0 | 99.0 | 93.5 | 91.7 | 83.8 | 77.2 | 71.2 |
| ✓ | ✗ | 85.6 | 99.8 | 99.4 | 99.0 | 95.9 | 94.8 | 89.4 |
| ✓ | ✓ | 98.8 | 99.9 | 99.6 | 99.3 | 99.1 | 96.8 | 97.0 |

Figure: Figure A5: Our estimated confidence scores are high correlated to the model performance on (a) distortion identification and (b) (c) instant rating tasks on different benchmarks in both full-reference and non-reference settings.
Refer to caption: x9.png

Ablation study on confidence estimation.
We further examine the correlation between model performance and estimated confidence scores on a wider range of benchmarks.
The results are illustrated in Fig. A5.
The performance of our model is consistently enhanced as the confidence interval increases, validating the effectiveness of our confidence estimation.

Figure: Figure A6: An example of the model-restored image.
Refer to caption: extracted/5662701/supp_figs/comp_modelproc/example.png

Details of quality comparison on model-processed images.
We consider five common image restoration tasks: super-resolution, denoising, JPEG compression artifact removal, motion deblurring, and defocus deblurring. For each task, we collect three to four cutting-edged models in recent years (listed in Tab. A4), apply them to a correspondingly degraded image, and then manually rank the resultant model-processed images. To find the image considered best by VLMs, we linearly scan the candidates and compare them in pairs. As VLMs’ results are not deterministic and may be sensitive to the presentation order of images, we repeat the linear scan 10 times and randomly shuffle the scan order each time.

We have shown that our DepictQA-Wild can consistently find the near-optimal candidate compared to GPT-4V and scored-based methods. A highlight is that our DepictQA-Wild generalizes well on these out-of-distribution (OOD) model-processed images. For example, the image in Fig. A6 is restored from a noisy image. There is still remnant noise, which is somewhat strange. For such an OOD image, our DepictQA-Wild correctly recognizes it to be inferior, but MANIQA [], MUSIQ [], and NIQE [] consider it as the best of the four candidates.

**Table A4: Image restoration models used in quality comparison on model-processed images.**
| Image restoration task | Image restoration models |
| --- | --- |
| Super-resolution | SwinIR [], HAT [], X-Restormer [] |
| Denoising | SwinIR []^1, MPRNet [], Restormer [], X-Restormer [] |
| JPEG compression artifact removal | SwinIR []^2, FBCNN ($q$=90) []^3, FBCNN (blind) []^4 |
| Motion deblurring | MAXIM [], MPRNet [], Restormer [] |
| Defocus deblurring | DRBNet [], IFAN [], Restormer [] |

More Qualitative Results.
More qualitative results of assessment reasoning, comparison reasoning, and assessment on web-downloaded images are presented in Fig. A7, Fig. A8, Fig. A9, Fig. A10, and Fig. A11.
DepictQA-Wild could accurately identify distortions, analyze their impacts on the display of image contents, then weigh the advantages and disadvantages of different aspects, and finally draw a final conclusion (e.g., overall quality, comparison results).

Figure: Figure A7: Qualitative results on assessment reasoning task in the full-reference setting. The two images from top to down are the reference image and evaluated image, respectively.
Refer to caption: x10.png

Figure: Figure A8: Qualitative results on assessment reasoning task in the non-reference setting.
Refer to caption: x11.png

Figure: Figure A9: Qualitative results on comparison reasoning task in the full-reference setting. The three images from left to right are the reference image, Image A, and Image B, respectively.
Refer to caption: x12.png

Figure: Figure A10: Qualitative results on comparison reasoning task in the non-reference setting. The two images from top to down are Image A and Image B, respectively.
Refer to caption: x13.png

Figure: Figure A11: Qualitative results on assessing web-downloaded images.
Refer to caption: x14.png

**Table A5: Question pool and answer pool of distortion identification task.**
| # | Question / Answer |
| --- | --- |
| 1 | Q: What are the primary degradation(s) observed in the evaluated image? |
| A: The primary degradation(s) in the evaluated image is/are {}. |  |
| 2 | Q: What distortion(s) are most apparent in the evaluated image? |
| A: The most apparent distortion(s) in the evaluated image is/are {} |  |
| 3 | Q: Identify the chief degradation(s) in the evaluated image. |
| A: The chief degradation(s) in the evaluated image is/are {}. |  |
| 4 | Q: Pinpoint the foremost image quality issue(s) in the evaluated image. |
| A: The foremost image quality issue(s) is/are {}. |  |
| 5 | Q: What distortion(s) stand out in the evaluated image? |
| A: The distortion(s) that stand out is/are {}. |  |
| 6 | Q: What distortion(s) are most prominent in the evaluated image? |
| A: The most prominent distortion(s) is/are {}. |  |
| 7 | Q: What critical quality degradation(s) are present in the evaluated image? |
| A: The critical quality degradation(s) presented is/are {}. |  |
| 8 | Q: Highlight the most significant distortion(s) in the evaluated image. |
| A: The most significant distortion(s) in the evaluated image is/are {}. |  |
| 9 | Q: What distortion(s) most detrimentally affect the overall quality of the evaluated image? |
| A: The distortion(s) that most detrimentally affect the overall quality is/are {}. |  |
| 10 | Q: Determine the most impactful distortion(s) in the evaluated image. |
| A: The most impactful distortion(s) in the evaluated image is/are {}. |  |
| 11 | Q: Identify the most notable distortion(s) in the evaluated image’s quality. |
| A: The most notable distortion(s) in the evaluated image’s quality is/are {}. |  |
| 12 | Q: What distortion(s) most significantly affect the evaluated image? |
| A: The distortion(s) that most significantly affect the evaluated image is/are {}. |  |
| 13 | Q: Determine the leading degradation(s) in the evaluated image. |
| A: The leading degradation(s) is/are {}. |  |
| 14 | Q: What distortion(s) are most prominent when examining the evaluated image? |
| A: The most prominent distortion(s) is/are {}. |  |
| 15 | Q: What distortion(s) are most evident in the evaluated image? |
| A: The most evident distortion(s) in the evaluated image is/are {}. |  |
| 16 | Q: What quality degradation(s) are most apparent in the evaluated image? |
| A: The most apparent quality degradation(s) is/are {}. |  |
| 17 | Q: In terms of image quality, what are the most glaring issue(s) with the evaluated image? |
| A: The most glaring issue(s) with the evaluated image is/are {}. |  |
| 18 | Q: What are the foremost distortion(s) affecting the evaluated image’s quality? |
| A: The foremost distortion(s) affecting the evaluated image’s quality is/are {}. |  |
| 19 | Q: Identify the most critical distortion(s) in the evaluated image. |
| A: The most critical distortion(s) is/are {}. |  |
| 20 | Q: In the evaluated image, what distortion(s) are most detrimental to image quality? |
| A: In the evaluated image, {} is/are the most detrimental distortion(s) to image quality. |  |
| 21 | Q: What are the most severe degradation(s) observed in the evaluated image? |
| A: The most severe degradation(s) is/are {}. |  |
| 22 | Q: What are the leading distortion(s) in the evaluated image? |
| A: The leading distortion(s) in the evaluated image is/are {}. |  |
| 23 | Q: What are the most critical image quality issue(s) in the evaluated image? |
| A: The most critical image quality issue(s) in the evaluated image is/are {}. |  |
| 24 | Q: What distortion(s) most notably affect the clarity of the evaluated image? |
| A: The distortion(s) that most notably affect the clarity is/are {}. |  |

**Table A7: Question pool and answer pool of instant rating task.**
| # | Question / Answer |
| --- | --- |
| 1 | Q: Which image do you believe has better overall quality: Image A or Image B? |
| A: I believe Image {} has better overall quality. |  |
| 2 | Q: Determine which image exhibits higher quality between Image A and Image B. |
| A: In my assessment, Image {} exhibits higher quality. |  |
| 3 | Q: Compare the general quality of Image A and Image B, and state your preference. |
| A: My preference leans towards Image {} to have better general quality. |  |
| 4 | Q: In your opinion, which image demonstrates superior quality: Image A or Image B? |
| A: In my opinion, Image {} demonstrates superior quality. |  |
| 5 | Q: Which of the two images, Image A or Image B, do you consider to be of better quality? |
| A: I consider Image {} to be of better quality. |  |
| 6 | Q: Evaluate the quality of Image A and Image B, and decide which one is superior. |
| A: I conclude that Image {} is superior. |  |
| 7 | Q: Between Image A and Image B, which image do you think has better quality overall? |
| A: I think Image {} has better quality overall. |  |
| 8 | Q: Determine which image, Image A or Image B, you perceive to have better quality. |
| A: I determine that Image {} has better quality. |  |
| 9 | Q: Assess the quality of Image A and Image B, and choose the one you believe is superior. |
| A: I choose Image {} to be superior in terms of quality. |  |
| 10 | Q: Which image stands out to you as having better quality: Image A or Image B? |
| A: Image {} stands out as the superior choice in terms of quality. |  |
| 11 | Q: Can you compare the quality of Image A and Image B and decide which one is better? |
| A: I find Image {} to be better after comparing the quality of both. |  |
| 12 | Q: Decide which image, Image A or Image B, you think possesses higher quality. |
| A: I decide that Image {} possesses higher quality. |  |
| 13 | Q: Evaluate Image A and Image B, and select the one that you feel has better quality. |
| A: Upon evaluation, I select Image {} as the one with better quality. |  |
| 14 | Q: Which of the two images, Image A or Image B, appears to have superior quality to you? |
| A: To me, Image {} appears to have superior quality. |  |
| 15 | Q: Compare the quality of Image A and Image B, and determine which one you prefer. |
| A: My preference leans towards Image {} after comparing the quality. |  |
| 16 | Q: Make a judgment on which image, Image A or Image B, you consider to be of better quality. |
| A: I consider Image {} to be of better quality. |  |
| 17 | Q: Between Image A and Image B, which image do you perceive to have better quality overall? |
| A: I perceive Image {} to have better quality overall. |  |
| 18 | Q: Assess the quality of Image A and Image B, and indicate which one you find to be better. |
| A: I find Image {} emerges as the better option with superior quality. |  |
| 19 | Q: Which image, Image A or Image B, do you think displays better quality when compared? |
| A: When compared, Image {} displays better quality. |  |
| 20 | Q: Differentiate between Image A and Image B in terms of overall quality and decide which one is superior. |
| A: Image {} differentiates itself with superior quality. |  |

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: See my comments above.
soundness: 2
strengths: The introduction of the DQ-495K dataset represents a significant contribution by providing a large-scale, high-quality dataset for Image Quality Assessment (IQA). The paper discusses various IQA tasks within both full-reference and no-reference frameworks, including assessments of single images and comparisons between paired images. Additionally, the incorporation of confidence estimation for responses is an interesting enhancement that could potentially improve the reliability of the assessments.
summary: In this paper, a large-scale EDQA-495K dataset is proposed to fine-tune VLMs as multi-functional image quality assessors that can handle both quality assessment and comparison tasks. In specific, they use a ground-truth-informed approach and retain image resolution during training to construct the dataset. Experimental results demonstrate the superiority of DepictQA-Wild over traditional score-based methods, prior VLM-based IQA models, and GPT-4V across various tasks and settings.
weaknesses: Although this paper contributes a comprehensive dataset and a multi-functional model for descriptive image quality assessment, the major weakness is the limited novelty and contribution, with some consideration that it is somewhat a data extension of the existing dataset and pipeline. Specifically, the pair-wise quality assessment and reasoning issue has been well addressed in the previous work Co-Instruct, while the performance gain of the proposed model basically comes from the augmented instruction data, which is somehow not a considerable contribution.  Besides, CoInstruct can also handle the quality comparison between multiple images, which is a harder task compared to image pairs, while the proposed model can only handle the latter one. Therefore, while the formulation of the augmented dataset is potentially useful, neither new perspectives nor new techniques are raised in this paper that can warrant a high score compared to previous work.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: See weaknesses.
soundness: 3
strengths: Strengths

1. The use of VLMs for IQA is new and aligns with current trends in AI research.
2. The authors propose a comprehensive and large-scale dataset, EDQA-495K, which could be a valuable resource for the research community.
3. The paper presents a clear and well-organized structure.
summary: The authors propose a method called Enhanced Descriptive Image Quality Assessment (EDQA), which aims to leverage a large-scale multi-modal dataset (EDQA-495K) for improved IQA. The methodology includes both single-image assessments and paired-image comparisons, supported by an extensive distortion library. The paper claims significant performance improvements over existing models.
weaknesses: 1. More detailed information is needed regarding the testing of the OOD settings presented in Table 3. Additionally, some non-reference IQA models, such as LIQE, are capable of identifying distortion types as well. It is recommended that these methods be included in the comparison.

2. Table 4 should incorporate IQA datasets that feature realistic distortions, such as KonIQ-10k and SPAQ. Including these datasets will allow for a more robust evaluation of the model's generalization capabilities.

3. The provided examples indicate that the model consistently generates template-like responses. It is important to investigate whether the model can effectively address open-ended questions. If so, results from the Q-Bench benchmark, which includes questions for both single and paired image inputs, should be provided.

4.  The methodology for computing quality score regression results in the full-reference setting (Table A3) is not clearly defined. Please elaborate on this aspect.

5.  Are there any specific fine-tuning procedures employed to obtain the results presented in Table A4?

6. Given that the model supports a maximum of two images as input, this limitation may lead to cumbersome and time-consuming comparisons when analyzing model-processed images across a larger variety of contents and algorithms. Suggestions for addressing this issue would be appreciated.

7. The results of Q-Instruct and Co-Instruct should also be included in Table 6. 

8. Are there any inherent limitations associated with this model? A detailed discussion on this topic would enhance understanding.

---

confidence: 5
contribution: 3
initial_rating: 8
presentation: 3
questions: The reviewers want to know the training resource consumption of the proposed model, as well as the inference efficiency. In addition, whether cross-modal and multi-functional IQA tasks are beneficial to classic instant rating IQA task.
soundness: 3
strengths: The authors have achieved multi-functional quality assessment through a general method, including quantitative indicators and qualitative language descriptions. The proposed method can adapt to a variety of practical application scenarios. The authors have achieved the purpose of multifunctional quality assessment based on VLM by reasonably designing data tags and prompts. The proposed multi-functional IQA task paradigm can provide support for subsequent work. The proposed model has achieved good performance on a variety of evaluation tasks, which shows its effectiveness.
summary: This paper proposes an Enhanced Descriptive image Quality Assessment method based on the VLM, and constructs a large-scale multimodal dataset. Compared with the existing VLMs-based IQA algorithms and the traditional score-based IQA algorithms, the authors adopt a general method to achieve multi-functional quality assessment, which can obtain brief and detailed responses. The authors conducted sufficient comparative experiments and ablation experiments to illustrate the superiority of the proposed method. From the experimental results, the proposed method is capable of coping with a variety of IQA tasks and can generate reasonable quality descriptions. This work is innovative and inspiring and the paper is well organized.
weaknesses: As shown in the experimental results，the proposed method shows good multi-functional IQA performance. However, the model size of the proposed model is much larger than that of the traditional IQA models, and the model training requires more computing resources. In particular, for the classic quantitative quality assessment task, the cross-modal training adopted by the proposed model does not seem to achieve significant improvement compared with the SOTA single-modal IQA model.

---

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. An investigation into comparison order effects should be conducted to verify the model’s robustness.
2. The model’s generalization capacity on distorted images in the wild should be examined.
soundness: 2
strengths: 1. The proposed model supports both full-reference and non-reference IQA tasks.
2. A large-scale dataset with 495k images and annotations is constructed, which will benefit VLM-based IQA model training.
3. Key tokens are analyzed for confidence score estimation, indicating the model’s uncertainty in its responses.
summary: In this paper, the authors propose Enhanced Descriptive image Quality Assessment (EDQA), a multi-functional IQA paradigm that addresses limitations in current VLM-based IQA methods. EDQA supports diverse tasks, including both full-reference and non-reference IQA scenarios and incorporates EDQA-495K—a large, high-quality dataset informed by ground-truth data. With image resolution retained, EDQA can handle resolution-dependent quality issues effectively, and it offers confidence scores to filter low-quality outputs.
weaknesses: 1. Only synthetic distortion types are included in the dataset construction. Although the authors test performance on the SPAQ dataset, inferior results compared to existing VLM models (e.g., Q-Align) raise concerns about the model’s functionality. Additional testing on authentic distortions is recommended to verify performance.
2. The paper lacks an investigation of comparison order effects. In full-reference IQA, the order of images (e.g., reference first, then distorted, or vice versa) significantly impacts model performance, and it’s crucial to verify model robustness in this scenario. The authors could consider using a small set of existing datasets or testing on the datasets released in [2AFC Prompting of Large Multimodal Models for Image Quality Assessment, TCSVT 2024] to strengthen performance validation.
3. While the authors state that image resolution is retained, the appendix reveals that images larger than 672 pixels are resized bilinearly, potentially diminishing the claimed resolution advantage. It is recommended that the authors refine this claim for accuracy.
4. Distortion severity in the constructed dataset appears limited. In real scenarios, distortion can be more severe (e.g., extreme low-light conditions), raising concerns about the model's generalization capability to such challenging, real-world conditions. It would be beneficial for the authors to test the model in real-world scenarios, including severe compression, low-light conditions, and adverse weather such as fog and rain, to better evaluate its practical applicability.

Meta-review: This paper proposes a multi-functional VLM-based IQA method. The proposed EDQA model can handle both the image quality assessment and comparison tasks, and full-reference and non-reference scenarios. The dataset is implemented with 35 types of synthetic distortions.

However, there were concerns that the dataset did not incorporate natural distortions.
There lacks a discussion on the use of synthetic and natural distortions.
Also, adding more details in the literature survey on datasets for distortion recognition could elaborate on the contribution of this work better. Currently, only 2 works without the released distortion lables are mentioned.
In the added evaluation on SPAQ / KoniQ dataset in Table A6, EDQA-LoRA trained on SPAQ dataset is only marginally better than Q-Align-LoRA while Q-Align-LoRA trained on SPAQ outperforms EDQA-LoRA original by a larger gap.

Meanwhile, while the scale of the dataset is one of the major contribution, the details of scaling up are not very clear other than citing You et al., 2023. 

Another major concern is the limited novelty. Compared with an earlier work, Co-Instruct, this work seems to be incremental although the results are better. Basically, the dataset could be summarized as using the images from KADIS-700K dataset and using ChatGPT to create candidate questions, applying synthetic distortions and the corresponding templated responses.  
While the authors state in the discussion with BK5E that creating high-quality data is not as trivial as simply combining more data, 
that seems to describe the generation process of the proposed dataset. 

To summarize, this paper could be improved by adding more analysis and discussion on the employment of real and synthetic distortions, by adding more extensive literature survey, and by better justifying the novelty of the contribution.

Decision: Reject