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\begin{document}

\title{Color-Aware Natural Scene Statistics for Enhanced No-Reference Assessment of Contrast-Distorted Images\\
}

\author{\IEEEauthorblockN{1\textsuperscript{st} Yusra Al Najjar}
\IEEEauthorblockA{\textit{Cybersecurity Department} \\
\textit{Zarqa University}\\
Zarqa, Jordan \\
yalnajjar@zu.edu.jo}
\and
\IEEEauthorblockN{2\textsuperscript{nd} Amer Rawash}
\IEEEauthorblockA{\textit{Artificial Intelligence Department} \\
\textit{Zarqa University}\\
Zarqa, Jordan \\
aalrwash@zu.edu.jo}
\and
\IEEEauthorblockN{3\textsuperscript{rd} Abdulla Al-Ali}
\IEEEauthorblockA{\textit{Computer Science Department} \\
\textit{Zarqa University}\\
Zarqa, Jordan \\
Aalali@zu.edu.jo}

}

\maketitle

\begin{abstract}
No-reference image quality assessment (NR-IQA) is crucial for evaluating perceptual quality without reference images. Existing NR-IQA models for contrast-distorted images primarily rely on luminance-based Natural Scene Statistics (NSS), often neglecting chromatic information. This study introduces two perceptually motivated color features—colorfulness (CIELab) and color naturalness (CIELuv)—into the NR-IQA framework. Experiments on three benchmark databases (TID2013, CID2013, and CSIQ) demonstrate that incorporating these color features consistently improves predictive accuracy, with up to 30\% higher PLCC and notable reductions in RMSE. These findings confirm that color cues complement luminance-based features and enhance the reliability of contrast-distortion assessment.
\end{abstract}

\begin{IEEEkeywords}
NR-IQA, NSS, Contrast distortions, image colorfulness, naturalness, perception quality metrics.
\end{IEEEkeywords}

\section{Introduction}
\IEEEPARstart{N}{o-reference} image quality assessment (NR-IQA) has gained prominence as digital images are increasingly shared and processed without corresponding reference images \cite{moorthy2011blind, mittal2012no}. 
Unlike full-reference IQA, which requires pristine originals, NR-IQA predicts perceptual quality directly from the distorted image.

Among various distortions, contrast degradation is particularly critical as it directly impacts visual visibility and interpretability \cite{Ponomarenko2013, Larson2010}. 
However, most NR-IQA models for contrast distortions rely mainly on luminance-based features, neglecting the perceptual role of color \cite{Chandler2013}. 
Since color perception strongly influences visual quality judgments \cite{Hasler2003}, its omission limits model accuracy.

\textbf{Problem Statement:} 
Existing NR-IQA methods for contrast distortions largely rely on luminance-based features and insufficiently capture chromatic contributions, limiting alignment with human perception. While deep learning approaches achieve high performance, they require large datasets, high computation, and are less interpretable. This work integrates perceptually motivated chromatic features into the NSS framework, providing a lightweight, explainable, and efficient NR-IQA approach. Bhuiyan and Khan~\cite{bhuiyan2018image} demonstrated that combining RMS contrast with histogram similarity improves NR-IQA performance, highlighting the value of chromatic features, while Kulkarni~\cite{kulkarni2023modified} underscores the importance of combining color and texture information to enhance image quality evaluation.



\section{Literature Review}

No-Reference Image Quality Assessment (NR-IQA) has been advanced by the Natural Scene Statistics (NSS) paradigm. Moorthy and Bovik~\cite{moorthy2011blind} and Mittal et al.~\cite{mittal2012no} showed that distortions disrupt the predictable statistics of natural images, enabling perceptual quality prediction.

NR-IQA research has emphasized contrast distortions due to their impact on image interpretability~\cite{larson2010most}. Fang et al.~\cite{fang2014no} proposed an NR-IQA model for contrast-distorted images (NR-IQA-CDI) using luminance statistics (mean, standard deviation, entropy, kurtosis, skewness), achieving strong performance. However, most NR-IQA-CDI methods rely primarily on luminance features, overlooking the role of color. Chandler~\cite{chandler2013seven} notes that incorporating color cues remains an open challenge, while color attributes such as saturation, hue, and colorfulness significantly affect perception~\cite{hasler2003measuring}.

Some studies have considered color in general-purpose NR-IQA. Hasler and Süsstrunk~\cite{hasler2003measuring} introduced a perceptual colorfulness metric, and Varga~\cite{varga2021no} used global statistical features including color descriptors. Nevertheless, explicit integration of perceptually relevant color features in CDI-focused models is limited. Table~\ref{tab:lit_review} summarizes representative works, showing the predominance of luminance features and limited color utilization. Our work addresses this gap by integrating colorfulness and naturalness features into an NSS-based NR-IQA-CDI framework~\cite{fang2014no}.

Deep learning methods have also become prominent. CNN- and transformer-based models, such as RankIQA~\cite{liu2017raniqa}, KonCept512~\cite{hosu2020koniq}, MUSIQ~\cite{ke2021musiq}, and TReS~\cite{zhu2022tres}, learn quality-aware features from large-scale datasets and often surpass traditional models. However, these methods require extensive data, high computational resources, and lack interpretability. In contrast, our color-aware NSS framework provides a lightweight, interpretable alternative, suitable for scenarios with limited training data and computational constraints.

\begin{table*}[htbp]
\centering
\caption{Summary of representative NR-IQA methods and their feature characteristics}
\label{tab:lit_review}
\begin{tabular}{l l l c}
\toprule
\textbf{Study} & \textbf{Distortion Type} & \textbf{Feature Basis} & \textbf{Color Features} \\
\midrule
Moorthy \& Bovik (2011)~\cite{moorthy2011blind} & General distortions & NSS (luminance) & No \\
Mittal et al. (2012)~\cite{mittal2012no} & General distortions & NSS (BRISQUE) & No \\
Fang et al. (2014)~\cite{fang2014no} & Contrast distortions & NSS (luminance statistics) & No \\
Chandler (2013)~\cite{chandler2013seven} & General IQA & Human visual factors & Limited \\
Hasler \& Süsstrunk (2003)~\cite{hasler2003measuring} & General images & Colorfulness metric & Yes \\
Varga (2021)~\cite{varga2021no} & General distortions & Global statistics + color & Yes \\
\midrule
Liu et al. (2017)~\cite{liu2017raniqa} & General distortions & Deep CNN (ranking-based) & Implicit \\
Hosu et al. (2020)~\cite{hosu2020koniq} & General distortions & Deep CNN (KonCept512) & Implicit \\
Ke et al. (2021)~\cite{ke2021musiq} & General distortions & Multi-scale Vision Transformer (MUSIQ) & Implicit \\
Zhu et al. (2022)~\cite{zhu2022tres} & General distortions & Transformer + CNN hybrid (TReS) & Implicit \\
\midrule
\textbf{This work} & Contrast distortions & NSS + Colorfulness (CIELab) + Naturalness (CIELuv) & \textbf{Yes (novel for CDI)} \\
\bottomrule
\end{tabular}
\end{table*}

\section{Contributions}

This work makes the following contributions to NR-IQA for contrast-distorted images:

\begin{itemize}
    \item \textbf{Color-Aware NSS Model:} Integration of a perceptually motivated \textbf{colorfulness feature} with luminance-based Natural Scene Statistics (NSS) to develop a robust NR-IQA-CDI framework.
    
    \item \textbf{Empirical Validation:} Through preliminary and comprehensive experiments, we demonstrate that incorporating color \textbf{significantly improves} prediction accuracy, yielding statistically significant ($p<0.05$) gains in PLCC and SROCC and reductions in RMSE across TID2013, CSIQ, and CID2013.
    
    \item \textbf{Perceptual Insight:} Evidence that \textbf{color perception is critical} in assessing contrast distortions, highlighting the necessity of chromatic features for human-aligned quality evaluation and addressing a key limitation of prior luminance-focused NR-IQA-CDI methods.
\end{itemize}

\section{Analysing the Effect of Adding Color Features to
NR-IQA-CDI
}
Experiments on adding the colorfulness feature to NSS-based NR-IQA-CDI were conducted in two stages: preliminary and comprehensive, with the latter performed only if preliminary results were promising. This paper presents the design, results, discussion, and conclusions.

\subsection{Preliminary Experiment}

The preliminary experiment aimed to quickly evaluate whether adding a color-related feature improves NR-IQA-CDI performance in predicting human Mean Opinion Scores (MOS). Contrast, represented by image standard deviation, served as the non-color feature, while image colorfulness was the color-related feature. Both were used in raw form, without computing full NSS, to reduce computational cost.

Image colorfulness was computed by converting RGB images to the device-independent CIELab space, where color is represented by chromatic components $a^*$ and $b^*$. The chroma was calculated as:
\begin{equation}
    C^* = \sqrt{a^{*2} + b^{*2}}
\end{equation}
Figure~\ref{fig:flowchart} illustrates the experimental procedure.

\begin{figure*}[htp]
    \centering
    \includegraphics[width=.7\linewidth]{flowchart 300.png}
    \caption{Flowchart of the Experimental Procedure for Assessing Image Colorfulness}
    \label{fig:flowchart}
\end{figure*}

Colorfulness of each image was derived from the mean and standard deviation of the chroma:
\begin{equation}
    C_k = \text{mean}(C^*) + \text{std}(C^*)
\end{equation}

To evaluate generalization, $k$-fold cross-validation (CV) was applied, repeating the procedure with multiple random partitions. Test images were drawn from TID2013 (250 contrast-distorted), CID2013 (400)~\cite{virtanen2014cid2013}, and CSIQ (116)~\cite{WinNT}, each with corresponding MOS.

Predicted scores were assessed using four metrics: Pearson's linear correlation coefficient (PLCC), Spearman's rank-order correlation coefficient (SROCC), root mean square error (RMSE), and outlier ratio (OR). The OR measures robustness as the fraction of predictions deviating beyond twice the MOS standard deviation. 

Performance differences before and after adding colorfulness were computed for each $k$:
\begin{equation}
    pd_i = ctc_i - c_i
\end{equation}
where $c_i$ and $ctc_i$ are the metric values without and with colorfulness, respectively ($i=2,\dots,k$). The overall percentage difference across all folds and databases was calculated as:
\begin{equation}
    pdp = \frac{1}{n}\sum_{i=1}^{n} \frac{pd_i}{|c_i|}
\end{equation}
where $n$ is the total number of folds across all databases. Absolute values preserve the sign of changes (increase or decrease). Paired two-tailed $t$-tests ($\alpha=0.05$) were conducted using MATLAB's \texttt{ttest} function; $p<0.05$ indicates statistically significant differences.

\subsubsection{Results and Discussion of Preliminary Experiment}

Table~\ref{tab:PPC} shows the percentage differences for each performance metric, and Table~\ref{tab:p_values_prelim} lists the corresponding $p$-values from paired $t$-tests. 

For TID2013, PLCC and SROCC increased by 30\% and 21.33\%, respectively; RMSE decreased by 4\%, and OR increased by 2\%. All $p$-values were below 0.05, indicating statistically significant improvements. The large SROCC gain suggests that the standalone colorfulness feature captures a complementary perceptual signal absent in the luminance-only model. Normalization in the comprehensive experiment (Table~\ref{tab:CPC}) tempers this raw effect while stabilizing integration into the NSS framework.

For CID2013, PLCC and SROCC increased by 20\% and 25\%, and RMSE decreased by 9\%. OR was not available. All three $p$-values were below 0.05, confirming significance.  

For CSIQ, marginal changes were observed in PLCC (0.2\%), RMSE (-0.4\%), and OR (3\%), whereas SROCC increased by 13\%, with only the SROCC change being statistically significant.

Overall, across all databases, PLCC, SROCC, and OR increased by 17\%, 72.4\%, and 2\%, respectively, and RMSE decreased by 5\%. Paired $t$-tests confirmed statistically significant improvements in PLCC, SROCC, and RMSE, while the small change in OR, although statistically significant, was minor compared to other metrics.
\begin{table}[ht]
\caption{Percentage Difference in Performance Metrics for Colorfulness}
\centering
\begin{tabular}{l c c c c}
\hline
& PLCC & SROCC & RMSE & OR \\
\hline
TID2013 & 30\% & 21.33\% & -4\% & 2\% \\
CID2013 & 20\% & 25\% & -9\% & NA \\
CSIQ    & 0.2\% & 13\% & -0.4\% & 3\% \\
Overall & 17\% & 72.4\% & -5\% & 2\% \\
\hline
\end{tabular}
\label{tab:PPC}
\end{table}

\begin{table}[ht]
\centering
\caption{$p$-values for Paired $t$-test in the Preliminary Experiment}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\hline
Dataset & PLCC & SROCC & RMSE & OR \\
\hline
TID2013 & $1.457\times10^{-3}$ & $6.952\times10^{-7}$ & $4.480\times10^{-4}$ & $3.498\times10^{-2}$ \\
CID2013 & $8.576\times10^{-6}$ & $5.414\times10^{-6}$ & $1.124\times10^{-5}$ & NA \\
CSIQ    & $4.814\times10^{-1}$ & $2.228\times10^{-3}$ & $2.558\times10^{-1}$ & $7.042\times10^{-2}$ \\
ALL     & $7.427\times10^{-6}$ & $1.196\times10^{-8}$ & $4.477\times10^{-5}$ & $8.209\times10^{-3}$ \\
\hline
\end{tabular}%
}
\label{tab:p_values_prelim}
\end{table}

These results indicate that incorporating colorfulness enhances NR-IQA-CDI performance, motivating further validation in the comprehensive experiment.


\subsection{Comprehensive Experiment}

The colorfulness probability distribution function (PDF) was estimated using the SUN2012 database \cite{SUN}, containing 16,000 natural scene images. Colorfulness for each image was computed, and the PDF was estimated using MATLAB's \texttt{dfittool()} to fit various distributions. The Normal distribution was selected as it best matched the empirical data. The maximum point of this distribution was identified for normalization, and both the fitted distribution and maximum were saved in a MAT file. The NSS of colorfulness was computed as the normalized probability that a natural image exhibits a given colorfulness value.

The existing NR-IQA-CDI features—mean, standard deviation, entropy, kurtosis, and skewness—\cite{fang2014no, varga2021no} were calculated using Fang et al.'s MATLAB code (\texttt{NRIQACDI.m}). The NSS colorfulness feature was concatenated with these five features to form a six-dimensional vector per image. This vector was input to a Support Vector Regressor (SVR) with RBF kernel to predict MOS, with hyperparameters optimized via grid search during cross-validation. Performance with and without the colorfulness NSS feature was assessed using the same metrics as in the preliminary experiment.

\subsubsection{Results and Discussion of Comprehensive Experiment - Colorfulness}

Table~\ref{tab:CPC} reports the percentage difference in performance metrics after adding the colorfulness NSS feature, while Table~\ref{tab:p_values_comprehensive V} presents the corresponding $p$-values. For TID2013, PLCC and SROCC increased by 3.04\% and 4.92\%, RMSE decreased by 0.94\%, and OR increased by 1.78\%, with all $p$-values $<0.05$, indicating statistically significant improvements.
Figure \ref{fig:enter-label} shows the Distribution fitting process.
\begin{figure}[t]
\centering
\includegraphics[width=1\linewidth]{graph2 300.jpg}
\caption{Distribution fitting process using dfittool(). The Normal
distribution was selected for colorfulness normalization.}
\label{fig:enter-label}
\end{figure}
For CID2013, PLCC and SROCC decreased slightly (-0.36\% and -0.59\%) with RMSE increasing by 1.08\%. OR was not available. All $p$-values were below 0.05, suggesting the small changes were statistically significant.  

For CSIQ, PLCC and SROCC increased substantially (13.69\% and 13.42\%), RMSE decreased by 11.44\%, and OR decreased by 18.32\%, all statistically significant.  

Overall, across the three databases, PLCC and SROCC improved by 5.46\% and 5.92\%, respectively, while RMSE and OR decreased by 3.77\% and 8.27\%, confirming that incorporating colorfulness enhances NR-IQA-CDI performance.

\begin{table}[ht]
\centering
\caption{Percentage of Difference in the Performance of NR-IQA-CDI After Adding Colorfulness}
\begin{tabular}{l c c c c}
\hline
Dataset & PLCC & SROCC & RMSE & OR \\
\hline
TID2013 & 3.04\% & 4.92\% & -0.94\% & 1.78\% \\
CID2013 & -0.36\% & -0.59\% & 1.08\% & NA \\
CSIQ & 13.69\% & 13.42\% & -11.44\% & -18.32\% \\
Overall & 5.46\% & 5.92\% & -3.77\% & -8.27\% \\
\hline
\end{tabular}
\label{tab:CPC}
\end{table}

\begin{table}[ht]
\centering
\caption{$p$-values of Paired t-test on the Differences in Performance Metrics}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\hline
Dataset & PLCC & SROCC & RMSE & OR \\
\hline
TID2013 & $1.8771\times10^{-03}$ & $2.3720\times10^{-04}$ & $2.9945\times10^{-03}$ & $3.7059\times10^{-04}$ \\
CID2013 & $2.1894\times10^{-04}$ & $1.3613\times10^{-04}$ & $2.2987\times10^{-05}$ & NA \\
CSIQ & $5.5393\times10^{-12}$ & $2.4486\times10^{-10}$ & $2.2412\times10^{-11}$ & $2.1065\times10^{-08}$ \\
ALL & $1.0860\times10^{-04}$ & $5.0151\times10^{-05}$ & $3.2675\times10^{-04}$ & $1.8426\times10^{-02}$ \\
\hline
\end{tabular}%
}
\label{tab:p_values_comprehensive V}
\end{table}

\section{Analyzing the Effect of Adding Naturalness of Color to NSS Features in NR-IQA-CDI}
This section presents experiments conducted to evaluate the significance of using color naturalness as an NSS feature in NR-IQA-CDI algorithms.

\subsection{Experimental Design}
Experiments were conducted in two stages: preliminary and comprehensive. The comprehensive stage was performed only if preliminary results were promising.

\subsubsection{Preliminary Experiment}
The preliminary experiment tested whether adding the color naturalness feature to the contrast feature improves the prediction of Mean Opinion Scores (MOS). 

Naturalness was quantified in the CIEL$^*u^*v^*$ color space, where saturation is defined as $S_{uv} = C^*/L^*$, separating intensity ($L^*$) from chromaticity ($C^*$). Images were converted from sRGB $\rightarrow$ linear RGB $\rightarrow$ XYZ $\rightarrow$ CIELuv according to:

\begin{equation}
L^{*} = \begin{cases}
116(Y/Y_n)^{1/3} - 16 & \text{if } Y/Y_n > 0.008856 \\
903.3 (Y/Y_n) & \text{otherwise}
\end{cases}
\end{equation}

\begin{equation}
u^{*} = 13 L^* (\acute{u} - \acute{u_n}), \quad
v^{*} = 13 L^* (\acute{v} - \acute{v_n})
\end{equation}

\begin{equation}
\acute{u} = \frac{4X}{X + 15Y + 3Z}, \quad 
\acute{v} = \frac{9Y}{X + 15Y + 3Z}
\end{equation}

Chroma, hue, and saturation were then computed using:

\begin{equation}
C^* = \sqrt{u^{*2}+v^{*2}}, \quad h_{uv} = \arctan(v^*/u^*), \quad S_{uv} = C^*/L^*
\end{equation}

Naturalness was calculated using a Gaussian model per color segment~\cite{bishop2006pattern}:

\begin{equation}
N_{ij} = \exp\left(-0.5 \left(\frac{S_{ij}-\mu_j}{\sigma_j}\right)^2\right), \quad 
N_i = \frac{\sum_j n_{ij} N_{ij}}{\sum_j n_{ij}}
\end{equation}

Contrast was computed and combined with naturalness using an SVR, as illustrated in Figure~\ref{fig:steps_naturalness}.

\begin{figure}[h!]
    \centering
    \includegraphics[width=0.5\textwidth]{naturalness flowchart.jpg}
    \caption{Steps for computing naturalness index for each image}
    \label{fig:steps_naturalness}
\end{figure}

\subsubsection{Results and Discussion of Preliminary Experiment}
Table~\ref{tab:PNP} presents the percentage differences in performance metrics, and Table~\ref{tab:prelim_pval_naturalness} reports $p$-values. For TID2013, PLCC and SROCC increased by 36.77\% and 245.11\%, RMSE and OR decreased slightly, and $p$-values indicate significance for PLCC, SROCC, and RMSE. CID2013 and CSIQ also showed improvements, all statistically significant except OR where data was unavailable or $p>0.05$.

\begin{table}[h!]
\centering
\caption{Percentage of Difference in NR-IQA-CDI Performance After Adding Naturalness}
\label{tab:PNP}
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 36.77\% & 245.11\% & -2.60\% & -0.92\% \\
CID2013 & 7.19\% & 8.24\% & -3.32\% & NA \\
CSIQ & 47.09\% & 54.33\% & -16.82\% & -19.08\% \\
All & 30.35\% & 102.56\% & -7.58\% & -10.00\% \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h!]
\centering
\caption{$p$-values of Paired $t$-test for NR-IQA-CDI After Adding Naturalness}
\label{tab:prelim_pval_naturalness}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 1.9369e-06 & 1.4329e-04 & 9.7722e-08 & 1.3562e-01 \\
CID2013 & 2.9402e-06 & 3.4699e-05 & 2.1380e-06 & NA \\
CSIQ & 5.2761e-08 & 9.3435e-08 & 5.4663e-08 & 3.9545e-05 \\
All & 1.4056e-07 & 1.8961e-07 & 7.7812e-18 & 5.9903e-04 \\
\bottomrule
\end{tabular}%
}
\end{table}

\subsubsection{Comprehensive Experiment}
Naturalness, already derived probabilistically, was combined with the five NSS features via SVR. Performance was assessed as in the preliminary stage.

\subsubsection{Results and Discussion of Comprehensive Experiment}
Table~\ref{tab:comp_diff_nat} shows percentage differences, and Table~\ref{tab:comp_pval_nat} shows $p$-values. TID2013 saw small improvements in PLCC (3.53\%) and SROCC (0.17\%), CID2013 showed minor increases, and CSIQ showed larger gains. Overall, PLCC and SROCC increased by 7.00\% and 6.53\%, while RMSE and OR decreased by 5.03\% and 8.76\%. Statistical tests confirm significance in all but some minor metrics.

\begin{table}[h!]
\centering
\caption{Percentage of Difference in NR-IQA-CDI Performance After Adding Naturalness (Comprehensive)}
\label{tab:comp_diff_nat}
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 3.53\% & 0.17\% & -1.42\% & 0.52\% \\
CID2013 & 0.43\% & 0.73\% & -0.80\% & NA \\
CSIQ & 17.05\% & 18.67\% & -12.88\% & -18.04\% \\
All & 7.00\% & 6.53\% & -5.03\% & -8.76\% \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h!]
\centering
\caption{$p$-values of Paired $t$-test After Adding Naturalness (Comprehensive)}
\label{tab:comp_pval_nat}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 3.4687e-08 & 3.0591e-01 & 1.94e-10 & 2.4284e-01 \\
CID2013 & 2.6769e-07 & 4.6865e-07 & 6.1106e-06 & NA \\
CSIQ & 7.7416e-10 & 8.7699e-09 & 2.0458e-09 & 4.6181e-06 \\
All & 3.4713e-05 & 3.1253e-04 & 9.6593e-10 & 3.3669e-03 \\
\bottomrule
\end{tabular}%
}
\end{table}

\subsection{Conclusion}
The experiments confirm that adding color naturalness improves NR-IQA-CDI performance in predicting the quality of contrast-distorted images. Preliminary tests showed promising results, and comprehensive experiments verified that integrating naturalness with NSS features yields significant improvements.

\section{Effect of Adding Both Colorfulness and Naturalness}
This experiment evaluates the effect of adding both colorfulness and naturalness NSS features to the five original features of NR-IQA-CDI.

Table~\ref{tab:both_diff} shows the percentage difference in each performance metric, and Table~\ref{tab:both_pval} shows the corresponding $p$-values. For TID2013, PLCC and SROCC increased by 5.19\% and 5.28\%, RMSE decreased by 1.79\%, and OR increased slightly by 1.35\%. All $p$-values were below 0.05, indicating statistical significance. 

For CID2013, PLCC slightly decreased by 0.07\%, SROCC increased by 0.19\%, and RMSE decreased by 0.67\%. $p$-values indicated significance for SROCC and RMSE. CSIQ showed notable improvements in PLCC (19.18\%) and SROCC (20.50\%) and decreases in RMSE (-15.47\%) and OR (-16.78\%), all statistically significant.

\begin{table}[h!]
\centering
\caption{Percentage Difference in NR-IQA-CDI Performance After Adding Both Colorfulness and Naturalness}
\label{tab:both_diff}
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 5.19\% & 5.28\% & -1.79\% & 1.35\% \\
CID2013 & -0.07\% & 0.19\% & 0.67\% & NA \\
CSIQ & 19.18\% & 20.50\% & -15.47\% & -16.78\% \\
All three Databases & 8.10\% & 8.65\% & -5.53\% & -7.72\% \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h!]
\centering
\caption{$p$-values for Paired $t$-test After Adding Both NSS Features}
\label{tab:both_pval}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\toprule
Dataset & PLCC & SROCC & RMSE & OR \\
\midrule
TID2013 & 1.14e-4 & 1.30e-4 & 8.87e-5 & 7.67e-3 \\
CID2013 & 1.48e-1 & 4.82e-2 & 1.56e-3 & NA \\
CSIQ & 3.86e-13 & 1.07e-9 & 7.07e-13 & 3.78e-7 \\
All three Databases & 3.39e-5 & 3.55e-5 & 5.54e-6 & 1.37e-2 \\
\bottomrule
\end{tabular}%
}
\end{table}

To evaluate the combined effect relative to individual features, Table~\ref{tab:comp_both_vs_one_diff} reports percentage differences, and Table~\ref{tab:comp_both_vs_one_pval} reports the corresponding $p$-values.  

\begin{table}[h!]
\centering
\caption{Percentage Difference: Both Features vs. Colorfulness and Naturalness}
\label{tab:comp_both_vs_one_diff}
\begin{tabular}{l c c c c}
\toprule
 & PLCC & SROCC & RMSE & OR \\
\midrule
Both vs. Colorfulness & 2.42\% & 2.48\% & -1.95\% & 0.84\% \\
Both vs. Naturalness & 1.04\% & 2.03\% & -0.59\% & 0.10\% \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h]
\centering
\caption{$p$-values: Both Features vs. Single Feature}
\label{tab:comp_both_vs_one_pval}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l c c c c}
\toprule
 & PLCC & SROCC & RMSE & OR \\
\midrule
Both vs. Colorfulness & $1.65\times10^{-6}$ & $7.51\times10^{-5}$ & $1.15\times10^{-9}$ & $3.90\times10^{-1}$ \\
Both vs. Naturalness  & $2.32\times10^{-3}$ & $6.50\times10^{-4}$ & $2.12\times10^{-1}$ & $3.60\times10^{-1}$ \\
\bottomrule
\end{tabular}%
}
\end{table}



The results indicate that adding both features yields significant improvements over adding colorfulness or naturalness individually in terms of PLCC, SROCC, and RMSE. Improvements in OR were marginal and not statistically significant.

Overall, incorporating both colorfulness and naturalness features provides the best performance enhancement for NR-IQA-CDI.

\section{Conclusion}
This study proposed the integration of color-aware Natural Scene Statistics (NSS) features—specifically colorfulness and naturalness—into no-reference image quality assessment (NR-IQA) for contrast-distorted images. By computing these features in perceptually uniform color spaces (CIELab and CIELuv) and fusing them with traditional luminance-based NSS features, the enhanced NR-IQA-CDI model consistently improved correlation coefficients (PLCC, SROCC) and reduced prediction errors (RMSE, OR) across three benchmark databases. These results demonstrate that chromatic information provides complementary cues to luminance statistics, leading to more accurate perceptual quality predictions.

Although performance gains varied across datasets due to differences in content and acquisition, the findings underscore the value of incorporating color cues in NR-IQA models. Future work will focus on extending this framework to hybrid deep learning architectures and larger, more diverse image sets to further enhance generalizability, robustness, and interpretability.


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