# Learning Generalizable Perceptual Representations for Data-Efficient No-
Reference Image Quality Assessment

Suhas Srinath∗ Shankhanil Mitra∗ Shika Rao Rajiv Soundararajan
Indian Institute of Science
Bengaluru, India 560012
{suhass12, shankhanilm, shikarao<EMAIL_ADDRESS>
###### Abstract

No-reference (NR) image quality assessment (IQA) is an important tool in
enhancing the user experience in diverse visual applications. A major drawback
of state-of-the-art NR-IQA techniques is their reliance on a large number of
human annotations to train models for a target IQA application. To mitigate
this requirement, there is a need for unsupervised learning of generalizable
quality representations that capture diverse distortions. We enable the
learning of low-level quality features agnostic to distortion types by
introducing a novel quality-aware contrastive loss. Further, we leverage the
generalizability of vision-language models by fine-tuning one such model to
extract high-level image quality information through relevant text prompts.
The two sets of features are combined to effectively predict quality by
training a simple regressor with very few samples on a target dataset.
Additionally, we design zero-shot quality predictions from both pathways in a
completely blind setting. Our experiments on diverse datasets encompassing
various distortions show the generalizability of the features and their
superior performance in the data-efficient and zero-shot settings.

**footnotetext: Equal contribution.$\S$$\S$footnotetext:
https://github.com/suhas-srinath/GRepQ

##  1 Introduction

The increasing number of imaging devices, including cameras and smartphones,
has significantly increased the volume of images captured, edited, and shared
on a global scale. As a result, there is a necessity to assess the quality of
visual content to enhance user experience. Image Quality Assessment (IQA) is
generally divided into two categories: full-reference (FR) and no-reference
(NR) IQA. While FR-IQA relies on pristine reference images for quality
assessment, NR-IQA is more relevant and challenging due to the absence of
clean references for user-captured images.

Most successful NR IQA methods are deep-learning based, and require a large
number of images with human opinion scores for training. As imaging systems
evolve, the distortions also evolve, making it difficult to keep creating
large annotated datasets for training NR IQA models. This motivates the study
of limited data or data-efficient NR IQA models which can be trained on a
target IQA application (or database) with limited labels. Such an approach
works best if the learned quality representations can generalize well across
different distortion types for various IQA tasks. These representations can
then be mapped to quality using a simple linear model [16, 25] using the
limited labels on a target application. Further, it is desirable that we learn
these representations without requiring any human annotations of image
quality. The goal of our work is to learn generalizable image quality
representations through self-supervised learning to design data-efficient NR
models for a target IQA application.

In this regard, while DEIQT[22] studies the data-efficient IQA problem, they
train the entire network with millions of parameters, which still requires a
reasonable number of labeled training images. On the other hand, recent
methods such as CONTRIQUE [16], Re-IQA [25], and QPT [4] focus on self-
supervised contrastive learning to learn quality features, which can
potentially yield superior performance with limited labels. However, these
methods do not consider that images with different distortions could have the
same quality, thereby limiting the generalizability of their image features
across varied distortions.

Our main contribution is the design of Generalizable Representations for
Quality (GRepQ) that can predict quality by training a simple linear model
with few annotations. We present two sets of features, one to capture the
local low-level quality variations and another to predict quality using the
global context. To capture low-level quality features, we propose a quality-
aware contrastive learning strategy guided by a perceptual similarity measure
between distorted versions of an image. In particular, we bring similar-
quality images closer in the latent space irrespective of their distortion
types. This is achieved by assigning a weight based on a similarity measure
between every pair of distorted versions of an image. Our strategy enables the
learning of generalizable quality representations invariant to distortion
types.

We also leverage the generalization capabilities of large vision-language
models for extracting high-level quality information. Notably, the versatile
CLIP[23] model can be applied to zero-shot quality prediction [28], although a
lack of task-specific fine-tuning limits it. While LIQE [42] fine-tunes CLIP
by integrating scene and distortion information, it requires large-scale
training with human labels. We overcome these limitations through a novel
unsupervised fine-tuning of CLIP. We achieve this by segregating images of
higher and lower quality into groups using antonym text prompts and employing
a group-contrastive loss with respect to the prompts. Our group-contrastive
learning facilitates the learning of high-level quality representations that
can generalize well to diverse content and distortions.

The features from both pathways can be combined to learn a simple regressor
trained with few samples from any IQA dataset. Additionally, predictions can
be made in a zero-shot setting using the learned features, which can then be
combined to provide a single objective score. We show through extensive
experiments that our framework shows superior performance in both the data-
efficient as well as zero-shot settings. We summarize the main contributions
of our framework as follows:

  * •

A quality-aware contrastive loss that weighs positive and negative training
pairs using a “soft” perceptual similarity measure between a pair of samples
to enable representation learning invariant to distortion types.

  * •

An unsupervised task-specific adaptation of a vision-language model to capture
semantic quality information. We achieve this by separating higher and lower-
quality groups of images based on quality-relevant antonym text prompts.

  * •

Superior performance of our method over other NR-IQA methods trained using few
samples (data-efficient) on several IQA datasets to highlight the
generalizability of our features. Additionally, we show superior cross-
database prediction performance.

  * •

A zero-shot quality prediction method using the learned features and its
superior performance compared to other zero-shot (or completely blind)
methods.

##  2 Related Work

###  2.1 Supervised NR-IQA

Many popular supervised NR-IQA methods such as BRISQUE[17], DIIVINE[19],
BLIINDS[24], CORNIA[35] predict quality using hand-crafted natural scene
statistics based features. Such methods have succeeded when images contain
synthetic distortions but often suffer when the distortions are more complex
or authentic. To mitigate this, several deep learning-based methods have
emerged that are either trained in an end-to-end fashion [41, 3, 11] or use a
pre-trained feature encoder that can be fine-tuned for IQA [41]. Further,
transformer-based models have shown promise on authentic and synthetically
distorted images [6, 37, 29, 27]. Methods such as MetaIQA[44] employ meta-
learning to learn from synthetic data and adapt to real-world images
efficiently. A recent method, LIQE [42] adapts the CLIP model for IQA via
scene and distortion classification along with supervised fine-tuning on
several IQA datasets. However, the model requires multiple annotations per
image during training making the model infeasible when adapting to newer and
more complex datasets in the data-efficient regime.

###  2.2 Self-Supervised Quality Feature Learning

Although supervised NR-IQA methods have shown reasonable performance in
quality prediction, they still possess the limitation of requiring large
amounts of human annotations for training. One of the earliest approaches in
this domain was through the design of quality-aware codebooks [35]. Later,
different ranking-based methods were used for quality-aware pre-training [14].
Contrastive learning-based training such as CONTRIQUE [16], Re-IQA [25] and
QPT [4] learn quality representations by contrasting multiple levels of
synthetic distortions. While Re-IQA [25] also uses high and low-level
features, our method significantly differs from Re-IQA in how the low-level
and high-level features are designed. Further, all the above methods neither
consider the generalizability to unseen distortions nor do they consider the
data-efficient evaluation setting.

###  2.3 Zero-Shot or Completely Blind (CB) IQA

Another class of IQA methods are zero-shot or completely blind and do not
require any human opinions for their design. For example, NIQE [18] neither
requires training on a dataset of annotated images nor knowledge about
possible degradations. IL-NIQE [38] improves over NIQE by integrating other
quality-aware features based on Gabor filter responses, gradients, and color
statistics. However, both methods tend to fail on authentic and other complex
distortions. A recent method [2] learns deep features using contrastive
learning to predict quality without any supervision. However, the performance
shown on in-the-wild IQA datasets still provides scope for further
improvement. Leveraging the contextual information from CLIP[23], CLIP-IQA
[28] shows that a zero-shot application of the CLIP model can yield promising
quality predictions. However, zero-shot methods tend to have poorer
performance and motivate the use of limited labels on target IQA applications
to improve performance.

###  2.4 Data-Efficient IQA

IQA in the low-data setting remains relatively unexplored. Data-efficient
image quality assessment (DEIQT) [22] shows that IQA models can be efficiently
fine-tuned with very few annotated samples from a target dataset, enabling
generalization through data efficiency. Further, with a sufficient number of
training samples, data-efficient training can achieve performances of full
dataset supervision on multiple IQA datasets. However, DEIQT still requires
end-to-end fine-tuning of a transformer model, leading to increased training
times.

Figure 1: Illustration of the GRepQ framework. The low-level model,
$f_{\theta}$, is trained using multiple distorted versions of an image
$\mathbf{x}_{i}$ subjected to the fragment sampling operation $T(\cdot)$.
$\mathbf{x}_{i}^{j}$ denotes an anchor image. The perceptual similarity
measure $s(\cdot,\cdot)$ is used to weigh the feature similarities of pairs of
distorted images $\mathbf{x}_{i}^{k}$ and $\mathbf{x}_{i}^{l}$ in Eq. 1. The
high-level model, $g_{\phi}$, is trained using Eq. 3 after selecting groups of
features based on their cosine similarities with embeddings $\mathbf{z}_{g}$
and $\mathbf{z}_{b}$ of antonym text prompts that relate to higher and lower
quality respectively. Embeddings are obtained from the text-encoder
$h(\cdot).$

##  3 Method

We first describe our approach to learning generalizable low-level and high-
level quality representations. The overall framework is illustrated in Fig. 1.
We discuss how quality is predicted in the data-efficient and zero-shot
settings.

###  3.1 Low-Level Representation Model

Contrastive learning for image quality [16, 25, 4] discriminates images based
on varied types and levels of distortions to capture low-level information.
While this provides very good pre-trained feature encoders without learning
from human labels, images with different distortion types are often treated as
a negative pair with respect to an anchor image, and the features of such
images are pulled apart. This leads to two main issues. An image with a
different distortion type may have a similar perceptual quality as the anchor.
Secondly, since image representations with different distortion types are
separated, this hurts the model’s generalizability to represent unseen
distortions. The goal of our work is to address both these limitations through
a quality-aware contrastive learning loss.

We introduce a novel quality-aware contrastive loss, where positive and
negative pairs (pairs of images considered similar and dissimilar in quality,
respectively) are selected based on their perceptual similarity. This allows a
soft weighting such that a similarity weight close to one treats the pair of
images as positive and pulls their corresponding representations closer.
Similarly, image features are pulled apart when the perceptual similarity is
near zero. This differs from the way prior methods use contrastive learning.
In particular, our framework allows the selection of pairs regardless of their
distortions, allowing for generalization.

Image Augmentation: In order to train the feature encoder using contrastive
learning, we generate multiple synthetically distorted versions of a camera-
captured image and sample fragments from each image. Four synthetic
distortions are generated: blur, compression, noise, and color saturation, at
two levels each. Fragment sampling has proven effective in retaining the
global quality information of an image [32]. To obtain fragments, we divide an
image into grids, and random mini-patches are extracted from each of the grid
locations. The mini-patches are then stitched together to yield a single
fragmented image that is used to train the model. An augmentation is generated
by randomly sampling another set of mini-patches from the same image to obtain
another fragmented image. Note that this augmentation is quality preserving
and can be used as a hard-positive pair in contrastive loss.

Quality-Aware Contrastive Loss: We contrast multiple distorted versions of the
same scene to learn quality representations and mitigate content bias.
Consider a batch of $N_{b}$ images $\\{\mathbf{x}_{i}\\}_{i=1}^{N_{b}}$, where
each image has $D$ distorted versions. Let $\mathbf{x}_{i}^{j}$ and
$\mathbf{x}_{i}^{k}$ denote two distorted versions of an image
$\mathbf{x}_{i}$ where $j,k\in\\{1,2,\ldots,D\\}$. Let $\mathbf{z}_{i}^{j}$
and $\mathbf{z}_{i}^{k}$ be the respective unit-norm feature representations
obtained as $\mathbf{z}_{i}^{j}=f_{\theta}(T(\mathbf{x}_{i}^{j}))$ and
$\mathbf{z}_{i}^{k}=f_{\theta}(T(\mathbf{x}_{i}^{k}))$, where $T(\cdot)$ is
the fragment sampling operation and $f_{\theta}(\cdot)$ is the feature
encoder. Let $s(\cdot,\cdot):\mathbb{R}^{M\times N}\times\mathbb{R}^{M\times
N}\rightarrow[0,1]$ denote a perceptual similarity measure between two images
with the same content. Further, let
$p_{\tau}(\mathbf{z}_{1},\mathbf{z}_{2})=\text{exp}(\mathbf{z}_{1}\cdot\mathbf{z}_{2}/\tau)$.
We overcome the limitation of existing contrastive learning methods which
require hard positives and negatives through the above soft similarity measure
to label positives and negatives. The similarity measures the closeness of
distorted versions in terms of intrinsic quality attributes and provides a
confidence weight in contrastive loss.

Our quality-aware contrastive loss is given by
$\mathcal{L}_{QACL}=\sum_{i=1}^{N_{b}}\sum_{j=1}^{D}\mathcal{L}_{i}^{j}$,
where $\mathcal{L}_{i}^{j}$ is given by

$\mathcal{L}_{i}^{j}=-\text{log}\frac{p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{j+})+\sum_{k\neq
j}s(\mathbf{x}_{i}^{j},\mathbf{x}_{i}^{k})p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{k})}{p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{j+})+\sum_{k\neq
j}p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{k})},$ (1)

where $\mathbf{z}_{i}^{j+}$ is the representation of an augmentation of the
image $\mathbf{x}_{i}^{j}$, and $\tau_{1}$ is a temperature hyperparameter.
Note that since
$p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{k})=s(\mathbf{x}_{i}^{j},\mathbf{x}_{i}^{k})p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{k})+(1-s(\mathbf{x}_{i}^{j},\mathbf{x}_{i}^{k}))p_{\tau_{1}}(\mathbf{z}_{i}^{j},\mathbf{z}_{i}^{k})$,
$\mathbf{x}_{i}^{k}$ is treated as similar to $\mathbf{x}_{j}^{k}$ with weight
$s(\mathbf{x}_{i}^{j},\mathbf{x}_{i}^{k})$ and dissimilar with weight
$(1-s(\mathbf{x}_{i}^{j},\mathbf{x}_{i}^{k}))$. The similarity function makes
the learning distortion type agnostic since it measures relative degradation
without knowledge of the distortion type, making the learned features
generalizable to different (and unseen) distortions. The InfoNCE [20] loss can
be seen as a special case of $\mathcal{L}_{QACL}$ when $s(\cdot,\cdot)=0$.

It is desirable that the perceptual similarity measure used satisfies a few
properties: ($1$) It captures intrinsic quality-specific attributes, such as
structure, sharpness, or contrast, ($2$) It is capable of handling various
distortion types used during training and correlates well with human judgments
on these distortions, ($3$) It captures local and global quality information
relevant to the human visual system, and $(4)$ It predicts similarity with
fairly low complexity to enable faster training times. We explore different
similarity measures such as FSIM [39], SSIM [30], GMSD [33], MS-SSIM[31] and
LPIPS[40] in our work. Degraded reference IQA [1] has also shown that such
similarity measures can be used to distinguish between distorted versions of a
degraded reference image. Finally, we note that such similarity measures can
be used to compare different distorted versions of an anchor image since all
of them have the same content. Thus, we do not include variations of different
images in this loss.

###  3.2 High-Level Representation Model

To understand the scene context for IQA, we adapt the CLIP model to the IQA
task. Image quality can be obtained using CLIP by measuring the cosine
similarity between the image feature and the text embeddings of a pair of
antonym prompts such as [‘‘a good photo.’’, ‘‘a bad photo.’’] [28]. Although
CLIP has a reasonable zero-shot quality prediction performance in terms of
correlation with human opinion, the representations are not specifically
crafted for the task of IQA, leading to a performance gap. We bridge this gap
by fine-tuning the image encoder of CLIP through an unsupervised loss, as
described below.

Contrastive Learning Over Groups: Analyses of vision-language models show that
text representations are richer than image representations [13, 21]. Thus, we
fix the text encoder in the CLIP model and only update the image encoder for
the IQA task. The text representations corresponding to the antonym prompts
remain the same during training and testing. We propose a loss for updating
the image encoder that aims at separating images in a batch into groups based
on how close the image representations are to two text-prompt embeddings. We
then seek to align the representations of images within each group and
separate the representations across groups. Such a loss simultaneously ensures
that the intra-group feature entropy (entropy of representations within each
group) is minimized and the inter-group entropy (entropy of features between
groups) is maximized [43, 10].

Consider a batch consisting of $N$ images $\\{\mathbf{x}_{i}\\}_{i=1}^{N}$
with visual representations $\\{\mathbf{z}_{i}\\}_{i=1}^{N}$. The
representations are obtained as $\mathbf{z}_{i}=g_{\phi}(\mathbf{x}_{i})$,
where $g_{\phi}(\cdot)$ is the CLIP image encoder. Let $\mathbf{z}_{g}$ and
$\mathbf{z}_{b}$ correspond to the prompt representations of ‘‘a good photo.’’
and ‘‘ bad photo.’’ respectively. We construct two groups of images,
$\mathcal{S}_{g}$ and $\mathcal{S}_{b}$, that correspond to higher and lower
quality respectively based on the quality estimated as

$Q_{H}(\mathbf{x}_{i})=\frac{1}{1+\text{exp}(k_{2}(\mathbf{z}_{i}\cdot\mathbf{z}_{b}-\mathbf{z}_{i}\cdot\mathbf{z}_{g}))},$
(2)

where $k_{2}$ is a scaling parameter. Let the features
$\\{\mathbf{z}_{i}\\}_{i=1}^{N}$ sorted in increasing order of $Q_{H}$ be
$\\{\mathbf{z}_{(1)},\mathbf{z}_{(2)},\cdots,\mathbf{z}_{(N)}\\}$. We obtain
the groups as $\mathcal{S}_{b}=\\{z_{(i)}\\}_{i=1}^{M}$ and
$\mathcal{S}_{g}=\\{z_{(i)}\\}_{i=N-M+1}^{N}$, where $M=\texttt{round}(N/k)$,
$k$ is a hyperparameter that decides the separability of lower and higher
quality groups within a batch of images, and $M$ denotes the group size. Let
$\mathcal{S}_{b}(i)=\mathcal{S}_{b}\setminus\\{\mathbf{z}_{i}\\}$ and
$\mathcal{S}_{g}(i)=\mathcal{S}_{g}\setminus\\{\mathbf{z}_{i}\\}$. Our group
contrastive loss used for fine-tuning is expressed as

$\displaystyle\mathcal{L}_{GCL}=$
$\displaystyle-\sum_{\mathbf{z}_{i}\in\mathcal{S}_{g}}\text{log}\frac{\sum_{\mathbf{z}_{j}\in\mathcal{S}_{g}(i)}p_{\tau_{2}}(\mathbf{z}_{i},\mathbf{z}_{j})}{\sum_{\mathbf{z}_{j}\in\mathcal{S}_{g}(i)\cup\mathcal{S}_{b}}p_{\tau_{2}}(\mathbf{z}_{i},\mathbf{z}_{j})}$
$\displaystyle-\sum_{\mathbf{z}_{i}\in\mathcal{S}_{b}}\text{log}\frac{\sum_{\mathbf{z}_{j}\in\mathcal{S}_{b}(i)}p_{\tau_{2}}(\mathbf{z}_{i},\mathbf{z}_{j})}{\sum_{\mathbf{z}_{j}\in\mathcal{S}_{b}(i)\cup\mathcal{S}_{g}}p_{\tau_{2}}(\mathbf{z}_{i},\mathbf{z}_{j})}.$
(3)

While creating groups, a quality separation gap and closeness of quality
scores of images in each group are necessary for effective contrastive
learning. The parameter $k$ controls this separability and is a hyperparameter
that needs to be appropriately chosen.

###  3.3 Mapping Representations to Objective Quality

Data-Efficient Quality Prediction: Once the high and low-level features are
learned, they are concatenated and regressed with mean opinion scores on the
evaluation datasets using a few samples from each dataset. We use a linear SVR
$f_{d}(\cdot):\mathbb{R}^{P}\rightarrow\mathbb{R}$ on features of target
datasets, where $P$ is the feature dimension. The data-efficient quality of
any new image $\mathbf{x}$ can simply be computed using its corresponding
feature representation $\mathbf{z}_{\mathbf{x}}\in\mathbb{R}^{P}$ as

$\mathbf{GRepQ}_{D}(\mathbf{x})=f_{d}(\mathbf{z}_{\mathbf{x}}).$ (4)

Our approach offers the advantage of requiring no end-to-end training using
the limited labels on a new target database.

Zero-Shot Quality Prediction: We use different approaches for the low-level
and high-level representations to predict quality without using any
supervision. For the low-level features, we compute a distance between the
features of the input image and that of a corpus of pristine images similar to
NIQE as

$d(\mathbf{x})=\sqrt{(\mu_{p}-\mu_{d})^{T}\bigg{(}\frac{\Sigma_{p}+\Sigma_{d}}{2}\bigg{)}^{-1}(\mu_{p}-\mu_{d})},$
(5)

where $\mu_{p}$ and $\Sigma_{p}$ are the mean and covariance of the
representations from the low-level encoder corresponding to patches of
pristine images. $\mu_{d}$ and $\Sigma_{d}$ are the mean and covariance of the
representations of the patches from an image $\mathbf{x}$. Here, non-
overlapping patches of size $R\times R$ are extracted from the image to
estimate the relevant statistics of the features. The low-level quality is
then predicted as

$Q_{L}(\mathbf{x})=\frac{1}{1+\text{exp}(k_{1}d(\mathbf{x}))},$ (6)

where $k_{1}$ is a scaling parameter. The quality from the high-level
representations can be predicted using Eq. 2. The overall image quality is
then measured as

$\text{$\mathbf{GRepQ}_{Z}$}(\mathbf{x})=Q_{H}(\mathbf{x})+Q_{L}(\mathbf{x}),$
(7)

and is illustrated in Fig. 2.

Figure 2: $\text{GRepQ}_{\text{Z}}$: Computing zero-shot image quality. The
text-prompts used for evaluation are "a good/bad photo.".

##  4 Experiments

###  4.1 Training and Implementation Details

Training Dataset for Representation Learning: We train the low and high-level
feature encoders on the FLIVE dataset [36] using a subset of $5000$ real-world
images encompassing a variety of authentic distortions with different
resolutions and aspect ratios. The diverse content and distortions make it
conducive to learning representations that can be generalized to diverse
images. No human annotations were used during this training.

Low-Level Encoder: We use a ResNet18 (performance with a Resnet50 was found to
be similar) without pre-trained weights for the low-level feature encoder. The
contrastive loss in Eq. 1 is trained by projecting the features from the
penultimate layer of ResNet18 onto $\mathbb{R}^{128}$. Images are fragmented
into $7\times 7$ grids, and random mini-patches from each grid location are
stitched together to form $224\times 224$ sized patches. The temperature
$\tau_{1}$ is fixed at $0.5$. A batch consists of $8$ images with $8$
distorted versions each. The model is trained for $15$ epochs using the AdamW
[15] optimizer with a weight decay of $0.05$ and an initial learning rate of
$10^{-4}$. A cosine learning rate scheduler is used. To guide the quality-
aware contrastive training, we employ FSIM as the perceptual similarity
measure.

Method Method Type CLIVE KonIQ CSIQ LIVE PIPAL Labels 50 100 200 50 100 200 50
100 200 50 100 200 50 100 200 TReS[6] End-to- end Fine Tuning 0.670 0.751
0.799 0.713 0.719 0.791 0.791 0.811 0.878 0.901 0.927 0.957 0.186 0.349 0.501
HyperIQA[27] 0.648 0.725 0.790 0.615 0.710 0.776 0.790 0.824 0.909 0.892 0.912
0.929 0.102 0.302 0.379 DEIQT[22] 0.667 0.718 0.812 0.638 0.682 0.754 0.821
0.891 0.941 0.920 0.942 0.955 0.396 0.410 0.436 MANIQA[34] 0.642 0.769 0.797
0.652 0.755 0.810 0.794 0.847 0.874 0.909 0.928 0.957 0.136 0.361 0.470
LIQE[42] 0.691 0.769 0.810 0.759 0.801 0.832 0.838 0.891 0.924 0.904 0.934
0.948 - - - Resnet50[8] Simple Feature Regre- ssion 0.576 0.611 0.636 0.635
0.670 0.707 0.793 0.890 0.935 0.871 0.906 0.922 0.150 0.220 0.302 CLIP[23]
0.664 0.721 0.733 0.736 0.770 0.782 0.841 0.892 0.941 0.896 0.923 0.941 0.254
0.303 0.368 CONTRIQUE[16] 0.695 0.729 0.761 0.733 0.794 0.821 0.840 0.926
0.940 0.891 0.922 0.943 0.379 0.437 0.488 Re-IQA[25] 0.591 0.621 0.701 0.685
0.723 0.754 0.893 0.907 0.923 0.884 0.894 0.929 0.280 0.350 0.431
$\text{GRepQ}_{\text{D}}$ (LL) 0.531 0.565 0.613 0.620 0.647 0.679 0.794 0.805
0.832 0.866 0.880 0.886 0.395 0.410 0.431 $\text{GRepQ}_{\text{D}}$ (HL) 0.740
0.770 0.796 0.794 0.813 0.843 0.869 0.905 0.932 0.904 0.927 0.944 0.410 0.415
0.427 $\text{GRepQ}_{\text{D}}$ (HL + LL) 0.760 0.791 0.822 0.812 0.836 0.855
0.878 0.914 0.941 0.926 0.937 0.953 0.489 0.518 0.548

Table 1: SRCC performance comparison of $\text{GRepQ}_{\text{D}}$ with other
NR-IQA methods trained using few labels on various IQA databases. The methods
are segregated into end-to-end trained (top five) and feature-learning-based
(next four) methods. LL and HL correspond to low and high-level models
respectively. The best-performing methods are bolded.

High-Level Encoder: We fine-tune CLIP’s image encoder while keeping the text
encoder fixed. The image encoder consists of a Resnet50 backbone with an
additional attention-pooling layer. To enable contrastive learning over groups
specified in Eq. 3, a projection head is used to contrast features in
$\mathbb{R}^{128}$. The images are center-cropped to a size of $224\times
224$, and a batch size of $N=128$ is used. Based on the coarse predictions
obtained using Eq. 2, we use a separability hyperparameter $k=8$ to divide the
batch of images into groups of size $M=16$. Once the groups are formed, the
image encoder is trained using Eq. 3 with a temperature $\tau_{2}=0.1$. The
model is trained for $15$ epochs using an Adam optimizer with an initial
learning rate of $5\times 10^{-6}$. The scaling parameter $k_{2}$ is set to
$10$.

Zero-Shot Quality Prediction using Low-Level Encoder: For the zero-shot
quality prediction using Eq. 5, we select $125$ pristine image patches as used
in literature [2] (chosen based on sharpness and colorfulness). Patches of
size $96\times 96$ are extracted from the pristine images and the test image.
The scaling parameter $k_{1}$ is set to $0.01$.

All the implementations were done in PyTorch using two 11GB Nvidia GeForce RTX
2080 Ti GPUs.

###  4.2 Experimental Setup

We present the details of the two main evaluation settings: data-efficient
setting and the zero-shot setting. In the data-efficient setting, we train our
data-efficient framework $\text{GRepQ}_{\text{D}}$, using a few samples from
each evaluation dataset. We randomly split each evaluation dataset into 80%
and 20% and use the 20% subset for testing. We select a random subset of 50,
100, or 200 samples from the 80% for training a linear support vector
regressor (SVR) on the features. We use Spearman’s rank order correlation
coefficient (SRCC) between the objective and subjective scores to evaluate the
models’ performance. We report the median performance obtained across 10
splits of each evaluation dataset. The results with respect to Pearson’s
linear correlation coefficient (PLCC) are given in the supplementary. In the
zero-shot setting, no training on any evaluation dataset is required, and we
test on the entire evaluation dataset.

Evaluation Datasets: We choose a variety of datasets spanning different types
of distortions to demonstrate the effectiveness of our framework for the three
experimental settings. Since the training images are sampled from the FLIVE
dataset, we do not evaluate them on FLIVE. We evaluate two popular in-the-wild
datasets: CLIVE [5], KONiQ [9], and three synthetic or processed image
datasets: LIVE-IQA[26], CSIQ[12] and PIPAL[7]. CLIVE contains $1,162$ images
captured from multiple mobile devices. KonIQ-10K contains $10073$ in-the-wild
images. LIVE-IQA[26] contains $29$ scenes along with $779$ distorted images
containing JPEG compression, blur, noise, and fast-fading distortions.
CSIQ[12] consists of $30$ original images with $866$ distorted images with
blur, contrast, and JPEG compression distortions. PIPAL is a large IQA
database consisting of $23,200$ images with $40$ different distortions per
image, including GAN-generated artifacts, making this dataset very challenging
to evaluate.

###  4.3 Data-Efficient Setting

We compare $\text{GRepQ}_{\text{D}}$ with other state-of-the-art (SoTA) end-
to-end NR-IQA methods: TReS[6], HyperIQA[27], and MANIQA[34], the data-
efficient method DEIQT[22], and feature based methods: Resnet50[8], CLIP[23],
CONTRIQUE[16] and Re-IQA[25]. We note that LIQE[42] is not trainable on PIPAL
and thus its entry is left blank. For the methods requiring feature
regression, the SVR parameters are optimized to yield the best performances.
To ensure fair comparisons, the median performance of all methods over ten
train-test splits are reported.

Tab. 1 presents comparisons on the data-efficient training of
$\text{GRepQ}_{\text{D}}$ against other NR-IQA methods. The results indicate
that $\text{GRepQ}_{\text{D}}$ outperforms other methods on all datasets in
almost all three data regimes (50, 100, and 200 samples). We notice that
$\text{GRepQ}_{\text{D}}$ outperforms even end-to-end trained models despite
using a simple SVR. The superior performance over Re-IQA, which may also be
considered as an ensemble of two sets of features, demonstrates the
superiority of both our low and high level features. While it may appear that
the high-level model performs better than the low-level model in most of the
scenarios, we provide examples in Sec. 4.6, where the low-level model could
also be more accurate. Thus, there is a need for both the high and low-level
representations. As an extreme case, we also present results in the fully-
supervised setting in the supplement.

Method CLIVE KonIQ CSIQ LIVE PIPAL NIQE[18] 0.463 0.530 0.613 0.836 0.153 IL-
NIQE[38] 0.440 0.507 0.814 0.847 0.282 CL-MI[2] 0.507 0.645 0.588 0.663 0.303
CLIP-IQA[28] 0.612 0.700 0.690 0.652 0.261 $\text{GRepQ}_{\text{Z}}$ 0.740
0.768 0.693 0.741 0.436

Table 2: Performance comparison of $\text{GRepQ}_{\text{Z}}$ (zero-shot) with
other zero-shot methods on various IQA databases.

Training FLIVE KonIQ CLIVE LIVE CSIQ Testing CLIVE KonIQ CLIVE KonIQ CSIQ LIVE
HyperIQA 0.758 0.735 0.785 0.772 0.744 0.926 TReS 0.713 0.740 0.786 0.733
0.761 - CONTRIQUE 0.710 0.781 0.731 0.676 0.823 0.925 DEIQT 0.733 0.781 0.794
0.744 0.781 0.932 $\text{GRepQ}_{\text{C}}$ 0.774 0.815 0.774 0.792 0.770
0.893

Table 3: Cross-dataset performance of $\text{GRepQ}_{\text{C}}$ along with
other NR-IQA methods. Results for methods apart from CONTRIQUE are from [22].

###  4.4 Zero-Shot Setting

Since zero-shot methods are trained without human supervision, we compare
$\text{GRepQ}_{\text{Z}}$ with unsupervised or completely blind NR-IQA methods
such as NIQE [18], IL-NIQE [38], contrastive learning with mutual information
(CL-MI) [2], and CLIP-IQA [28]. We utilize entire evaluation databases for
testing all the methods.

Tab. 2 shows that $\text{GRepQ}_{\text{Z}}$ consistently outperforms other
methods on three out of five datasets by considerable margins.
$\text{GRepQ}_{\text{Z}}$ achieves SoTA performance even on the challenging
PIPAL dataset, containing diverse distortions, particularly images restored by
various restoration (including GAN-based) methods for super-resolution and
denoising. A $44\%$ improvement in SRCC is shown over the second-best-
performing algorithm (CL-MI). The lower performance of
$\text{GRepQ}_{\text{Z}}$ on LIVE and CSIQ is attributed to content bias of
both the low-level and high-level models. Although the low-level model is
trained in a content conditional manner, the features perhaps do suffer from
some residual content bias. Since LIVE and CSIQ contain very few unique scene
content, the residual content bias leads to reduced performance of our zero-
shot model. Despite these challenges, $\text{GRepQ}_{\text{Z}}$ still achieves
competitive performance, showing its generalization capability in the zero-
shot setting.

Similarity Measure | $50$ | $100$ | $200$
---|---|---|---
None | 0.381 | 0.413 | 0.452
SSIM | 0.533 | 0.558 | 0.590
MS-SSIM | 0.527 | 0.561 | 0.575
GMSD | 0.544 | 0.570 | 0.583
LPIPS | 0.578 | 0.605 | 0.629
FSIM | 0.620 | 0.647 | 0.679
Table 4: SRCC performance analysis on the KonIQ dataset of the impact of
different perceptual similarity measures on the low-level model under the
data-efficient setting.

###  4.5 Cross-Database Experiments

We also show the effectiveness of our features through cross-database
experiments. Here, a single linear SVR (ridge regressor) is trained on an
entire dataset and tested on other intra-domain datasets in the authentic and
synthetic image settings. The results in Tab. 3 indicate that
$\text{GRepQ}_{\text{C}}$ (defined as the cross-dataset prediction evaluated
using Eq. 4) achieves competitive (also best) performances in most of the
evaluation settings.

###  4.6 A Deeper Understanding of GRepQ Features

Choice of Perceptual Similarity Measures in the Low-Level Feature Encoder: We
compare different popular perceptual similarity measures such as SSIM [30],
MS-SSIM[31], FSIM[39], LPIPS[40] and GMSD[33] used in the low-level feature
encoder in Tab. 4. The low-level encoders are trained using these measures
under similar training settings. We also train an encoder without any
similarity measure (denoted by None) to show a need for quality-aware
contrastive learning. In this case, all the other distorted versions of an
image are treated as negatives, while the augmented version is the only
positive. In Tab. 4, we show the low-level encoder’s data-efficient
performances on KonIQ. We see that FSIM outperforms all other measures. We
note that the superior performance of FSIM in this context is consistent with
its superior performance as an FR-IQA metric across multiple datasets.

(a)

(b)

Figure 3: A visualization of feature representations of images from a
combination of CLIVE and the test set of KonIQ using t-SNE for features from
(a) the zero-shot CLIP image encoder and (b) our fine-tuned high-level
encoder. Blue and orange points correspond to bad and good-quality images
respectively.

|  |  |  |
---|---|---|---|---
(a) | (b) |  | (c) | (d)
28.97 | 24.49 |  Human Opinion Score | 75.63 | 65.15
40.56 | 25.43 |  $\text{GRepQ}_{\text{D}}$ (LL) Prediction | 62.04 | 61.29
27.66 | 39.40 |  $\text{GRepQ}_{\text{D}}$ (HL) Prediction | 72.33 | 49.85

Figure 4: Demonstrating the complementarity of high and low-level model
predictions. Images with different MOS from the KonIQ-10K database are listed.
Low and high-level model predictions are mentioned below their respective MOS.
Predictions that agree with human opinions are marked with green while
erroneous predictions are marked with red.

Analyzing High-Level Feature Representations: We analyze the impact of our
group-contrastive learning in improving the high-level quality
representations. For this analysis, we identify extremely good and extremely
bad quality images based on mean opinion score (MOS) greater than 75 or less
than 25 respectively on the combined CLIVE and KonIQ datasets. We show the
feature representations of the CLIP model in Fig. 3a and those of our model in
Fig. 3b. We see that our learned representations are better separable between
the higher and lower-quality images. This leads to the superior performance of
our high-level model when compared to CLIP-IQA.

Complementarity of High and Low-Level Features: We present a qualitative and
quantitative analysis of the complementarity of representations from both
encoders. We show examples of when the two models outperform each other in
Fig. 4. For instance, Fig. 4a shows that the low-level model makes an
erroneous prediction since only the object in focus is blurred, but the
background is relatively clean. Fig. 4d shows that the image does not contain
enough contextual information for the high-level model to make an accurate
prediction. We also perform an error-based feature complementarity analysis in
Fig. 5. In particular, we compute the absolute error between the MOS predicted
by the high and low-level models and the true MOS and show them in four
quadrants. We see several examples where one of the models performs much
better than the other. This shows that the models have complementary behavior
in many examples.

Limitations: In the low-data setting, the low-level model does not perform as
well as the high-level model on in-the-wild datasets. Since the low-level
model is more suited to capture varied distortion levels rather than content,
synthetic datasets benefit more from this model. Secondly, the high-level
model uses fixed prompts and can be further improved through prompt
engineering or tuning.

Figure 5: Analysis of absolute error in quality predictions from high and low-
level models on the KonIQ-10K database.

##  5 Concluding Remarks

We design generalizable low-level and high-level quality representations that
enable IQA in a data-efficient setting. Specifically, we learn low-level
features using a novel quality-aware contrastive learning strategy that is
distortion-agnostic. Secondly, we present a group-contrastive learning
framework that learns to elicit semantic-based high-level quality information
from images. We show that both sets of representations lead to accurate
prediction of quality scores in both the data-efficient and zero-shot settings
on diverse datasets. This demonstrates the generalizability of our learned
features. Future advances in self-supervised learning and quality-specific
prompt engineering could be used to further enhance the generalizability of
models for data-efficient NR IQA.

Acknowledgement: This work was supported in part by Department of Science and
Technology, Government of India under grant CRG/2020/003516.

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