# Full-Reference Image Quality Assessment Measure Based on Color Distortion

Zianou Ahmed Seghir, Fella Hachouf

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## To cite this version:

Zianou Ahmed Seghir, Fella Hachouf. Full-Reference Image Quality Assessment Measure Based on Color Distortion. 5th International Conference on Computer Science and Its Applications (CIIA), May 2015, Saida, Algeria. pp.66-77, 10.1007/978-3-319-19578-0_6 . hal-01789962

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# Full-reference image quality assessment measure based on color distortion

Zianou Ahmed Seghir¹, Fella Hachouf²

¹ University Khenchela, Faculty. ST,
ICOSI Lab., BP 1252 El Houria, 40004 Khenchela, Algeria

² Laboratoire d'Automatique et de Robotique, Université Constantine1, Algeria
e-mail: zianou_ahmed_seghir@yahoo.fr

**Abstract.**

The purpose of this paper is to introduce a new method for image quality assessment (IQA). The method adopted here is assumed to be Full-reference measure. Color images that are corrupted with different kinds of distortions are assessed by applying a color distorted algorithm on each color component separately. This approach use especially YIQ color space in computation. Gradient operator was successfully introduced to compute gradient image from the luminance channel of images. In this paper, we propose an alternative technique to evaluate image quality. The main difference between the new proposed method and the gradient magnitude similarity deviation (GMSD) method is the usage of color component for the detection of distortion.

Experimental comparisons demonstrate the effectiveness of the proposed method.

**Keywords:** Gradient similarity, quality assessment, test image, Color distortion, color space.

## 1 Introduction

Over the past decade, image quality assessment methods based on objective methods have grown significantly to tackle problems of image assessment. The challenge of these problems is to construct an algorithm that can automatically predict perceived quality of image.

There is no doubt that the subjective test is the most accurate measure for quality assessment because it reflects the true human perception. On the other hand, it is time consuming and expensive. There are three kinds of measures that are used for objective image quality assessment, full-reference (FR), reduced-reference (RR) and no-reference (NR). In this paper, the discussion is confined to FR metrics, where the reference images are available.

There has been extensive work on objective image quality assessment. The most popular method for full reference image quality assessment is the Structural Similarity Index [2] (SSIM). It contains three parts: Luminance Comparison, Contrast Comparison, and Motion Compensation.
son and Structure Comparison. However, it fails in measuring the badly blurred imag-
es [3]. In [4], an approach based on edge-region information, distorted and displaced
pixels (ERDDM) is developed. Initially, the test and reference images are divided into
blocks of 11×11 pixels, and then distorted and displaced pixels are calculated which
can be used to compute the global error. In [6], *DTex* metric is proposed with consid-
eration of the texture masking effect and contrast sensitivity function. In [17], it was
shown that the masking effect and the visibility threshold can be combined with struc-
ture, luminance and contrast comparison to create the image quality measure (gradient
similarity measure (GSM)). Most Apparent Distortion (MAD) designed in [23, 24]
yields two quality scores, i.e., visibility-weighted error and the differences in log-
Gabor subbands statistics. The proposed measure in [13] applies phase congruency
[15] to image quality measure. This measure differs in their correlations with the sub-
jective quality and carrying out times. Gradient magnitude similarity deviation
(GMSD) is proposed [14], where the pixel-wise gradient magnitude similarity (GMS)
is used to capture image local quality, and the standard deviation of the overall GMS
map is computed as the final image quality index.

The gradient images are sensitive to image distortions, whereas different local
structures in a distorted image suffer different degrees of degradations. This motivates
us to investigate the use of global variation of gradient based local quality map for
overall image quality prediction. In fact, color deformation cannot be well differenti-
ated by gradient. In addition, the gradient is computed from the luminance channel of
images. Therefore, to make the image quality assessment measures own the ability to
deal with color distortions, chrominance information should be taken into considera-
tion.

The aim of this paper is to improve the GMSD to take color distortion in consid-
eration. As a result, we use a proposed gradient operator and *YIQ* color space [1] to
produce gradient image and color distortion from the reference and test images, re-
spectively.

The rest of the paper is organized as follows. In Section 2, our proposed image
quality measure is defined. In section 3, performance of the proposed method is com-
pared with others measures using images with different types of distortion. We finish
by the conclusion.

## 2 Proposed method

Before introducing the proposed measure notion, some useful concepts must be
visited. The reference and test images are represented by *Ref* (*M*, *N*) and *Dis* (*M*, *N*)
respectively.

The proposed method uses gradient similarity and Color distortion to form map.
In addition, all variables used in the proposed method are defined next:

*Ref*: reference image.

*Dis*: test image.

*M × N*: the image size.

*G*<sub>*i*</sub>: gradient image of *Ref*.
$G_2$: gradient image of *Dis*.

*G_map*: Gradient similarity map.

*CFI_map* and *CFQ_map*: chromatic features.

$C_1, C_2$: positive constants.

*GSCDM*: Gradient similarity based Color distortion measure.

## 2.1 Gradient similarity

In order to reflect the differences between *Ref* and *Dis* at the local level, we compute image gradient of the reference and test images. Different operators are used to compute the image gradient, such as the Sobel operator [7], the Prewitt operator [7] and the Scharr operator [8], and in this paper a new gradient operator is proposed, which shows very favorable outcome. It defines as:

<table><thead><tr><th></th><th>Gx</th><th>Gy</th></tr></thead><tbody><tr><th>Mask</th><td>(<table><tbody><tr><td>4</td><td>0</td><td>-4</td></tr><tr><td>3</td><td>0</td><td>-3</td></tr><tr><td>4</td><td>0</td><td>-4</td></tr></tbody></table>)/11</td><td>(<table><tbody><tr><td>4</td><td>3</td><td>4</td></tr><tr><td>0</td><td>0</td><td>0</td></tr><tr><td>-4</td><td>-3</td><td>-4</td></tr></tbody></table>)/11</td></tr></tbody></table>

This later consists of a pair of 3×3 convolution kernels and is used for detecting vertical and horizontal edges in images.

The partial derivatives $Gx$ and $Gy$ of an image are computed as:

$$G = \sqrt{Gx^2 + Gy^2} \qquad (1)$$

Also, the gradient operators ($G$) of the reference and test images are computed. As a result, the $G_2$ and $G_1$ of the test and reference images are produced, respectively.

The gradient similarity is computed in proposed method and hence the Gradient map (*G_map*) is formed as

$$G\_map = \frac{2G_1 \cdot G_2 + C_1}{G_1^2 + G_2^2 + C_1} \qquad (2)$$

## 2.2 Color space transformation

The color distortion cannot be differentiating by gradient. Hence, to make the image quality assessment measures possess the ability to deal with color distortions, special considerations are given to chrominance information. As a result, these formulas approximate the conversion between the *RGB* color space and *YIQ* [1]

$$\begin{bmatrix} Y \\ I \\ Q \end{bmatrix} = \begin{bmatrix} 0.299 & 0.587 & 0.144 \\ 0.596 & -0.275 & -0.321 \\ 0.212 & -0.528 & 0.311 \end{bmatrix} \begin{bmatrix} R \\ G \\ B \end{bmatrix} \qquad (3)$$
Let $I_1$ ($I_2$) and $Q_1$ ($Q_2$) be the I and Q chromatic channels of the reference and dis-torted images respectively. Similar to the definitions of CFI_map and CFQ_map, the similarity between chromatic features is defined as follows:

$$CFI\_map = \frac{2I_1 \cdot I_2 + C_2}{I_1^2 + I_2^2 + C_2} \qquad (4)$$

$$CFQ\_map = \frac{2Q_1 \cdot Q_2 + C_2}{Q_1^2 + Q_2^2 + C_2}$$

The similarity between the chrominance components (color distortion map) is simply defined as:

$$CD\_map = CFI\_map \cdot CFQ\_map \qquad (5)$$

## 2.3 Global error

Finally, the gradient similarity based Color distortion map (GSCD_map) is expressed as:

$$GSCD\_map = G\_map \cdot CD\_map \qquad (6)$$

The total gradient similarity based Color distortion measure (GSCDM) is defined as the standard deviation of the GSCD map:

$$GSCDM = \sqrt{\frac{1}{N \cdot M} \sum_{p=1}^{M} \sum_{q=1}^{N} (GSCD - GSCD\_map(p, q))^2} \qquad (7)$$

Where

$$\overline{GSCD} = \frac{1}{N \cdot M} \sum_{p=1}^{M} \sum_{q=1}^{N} GSCD\_map(p, q) \qquad (8)$$

Flowchart depicting computation of the proposed measure is shown in Fig. 1.

# 3 Results

In order to evaluate the accuracy of the proposed method; we follow the standard performance assessment procedures utilized in the video quality expert's group (VQEG) FR-TV Phase II test [5]. The objective and subjective scores [5], are fitted with the logistic function. Five parameters non-linear mapping ($\theta_1$, $\theta_2$, $\theta_3$, $\theta_4$ and $\theta_5$) are utilized to change the set of quality ratings by the objective quality measures to a set of the predicted Difference Mean Opinion Score (DMOS/MOS) values denoted DMOSp/MOSp.

In equation (9), the logistic regression function is introduced which is employed for the nonlinear regression.

$$f(VQR) = \theta_1 \left( \frac{1}{2} - \frac{1}{\exp(\theta_2(VQR - \theta_3))} \right) + \theta_4 VQR + \theta_5 \qquad (9)$$
<figure><img src="image_3.png" /><figcaption>Fig. 1. Image quality assessment method</figcaption></figure>

Where VQR is the value of the objective method and $θ_1, θ_2, θ_3, θ_4, θ_5$ are selected for the most excellent fit.

In this test, four metrics are used [26]: the Root mean square prediction error (RMSE), the Spearman rank-order correlations coefficient (ROCC), Kendall rank-order correlation coefficient (KROCC) and The Pearson linear correlation coefficient (CC). ROCC and KROCC evaluate the prediction monotonicity. CC and RMSE assess the prediction accuracy. ROCC, KROCC and CC are better with values closer to 1 or -1. Thus, RMSE is better when its values are small.
The first index CC (Pearson linear correlation coefficient) is defined by:

$$CC = \frac{\sum_{i=1}^{n} (DMOS(i) - \overline{DMOS})(DMOS_{p}(i) - \overline{DMOS_{p}})}{\sqrt{\sum (DMOS(i) - \overline{DMOS})^2} \sqrt{\sum (DMOS_{p}(i) - \overline{DMOS_{p}})^2}} \quad (10)$$

Where the index i denotes the image sample and n denotes the number of samples. The second index is the Spearman rank-order correlations coefficient (ROCC); it is defined by:

$$ROCC = 1 - \frac{6 \sum (DMOS(i) - DMOS_{p}(i))^2}{n(n^2 - 1)} \quad (11)$$

The third index is Kendall rank-order correlation coefficient (KROCC) [25]. It is designed to capture the association between two ordinal variables. Its estimate can be expressed as follows:

$$KROCC = \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} sgn(DMOS(i) - DMOS(j)) sgn(DMOS_{p}(i) - DMOS_{p}(j))}{n(n-1)} \quad (12)$$

where:

$$sgn(DMOS(i) - DMOS(j)) = \begin{cases} 1 & \text{if } (DMOS(i) - DMOS(j)) > 0 \\ 0 & \text{if } (DMOS(i) - DMOS(j)) = 0 \\ -1 & \text{if } (DMOS(i) - DMOS(j)) < 0 \end{cases}$$

and

$$sgn(DMOS_{p}(i) - DMOS_{p}(j)) = \begin{cases} 1 & \text{if } (DMOS_{p}(i) - DMOS_{p}(j)) > 0 \\ 0 & \text{if } (DMOS_{p}(i) - DMOS_{p}(j)) = 0 \\ -1 & \text{if } (DMOS_{p}(i) - DMOS_{p}(j)) < 0 \end{cases}$$

The fourth one is the Root mean square prediction error (RMSE) between subjective (DMOS) and objective (DMOS<sub>p</sub>) scores. It is defined by:

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (DMOS(i) - DMOS_{p}(i))^2} \quad (13)$$

To judge the performance of the proposed approach, four kinds of databases are used: TID2008 database [9], CSIQ database [10], LIVE database [11] and TID2013 database [12]. The characteristics of these four databases are summarized in table 3.

The performance of GSCD metric is compared with PSNR, SSIM [2,16], Multiscale-SSIM (MS-SSIM) [18,16], Visual Singal-to-Noise Ratio (VSNR) [19,16], Visual Information Fidelity (VIF) [20,16], Information Fidelity Criterion (IFC) [21,16], Noise Quality Measure (NQM) [22, 16], DTex [6], GSM [17], MAD [23,24], ERDDM [4], GSMD [14] and FSIM [13].

A comparative study of Sobel, Perwitt, Scharr and proposed operator is presented in Table 1 (TID2008 database is used in this experience), from which proposed operator could accomplish better performance than the other three. Furthermore, the choice of YIQ color space needs to be proved. To this end, we run the proposed method with
different four color spaces. The results are summarized in table 2 (TID2008 database is used in this experience).

<table><caption>**Table 1.** ROCC and KROCC values using four gradient operators</caption><thead><tr><th>Gradient operator</th><th>Sobel</th><th>Perwitt</th><th>Scharr</th><th>Proposed operator</th></tr></thead><tbody><tr><td>ROCC</td><td>0.8983</td><td>0.8996</td><td>0.8963</td><td><strong>0.9000</strong></td></tr><tr><td>KROCC</td><td>0.7143</td><td>0.7171</td><td>0.7104</td><td><strong>0.7175</strong></td></tr></tbody></table>

<table><caption>**Table 2.** ROCC and KROCC values using four color spaces</caption><thead><tr><th>Color space</th><th>Lab</th><th>ycbcr</th><th>HSV</th><th>YIQ</th></tr></thead><tbody><tr><td>ROCC</td><td>0.7684</td><td>0.8937</td><td>0.2983</td><td><strong>0.9000</strong></td></tr><tr><td>KROCC</td><td>0.5789</td><td>0.7110</td><td>0.2125</td><td><strong>0.7175</strong></td></tr></tbody></table>

The classification of the performance of all measures according to their ROCC values is presented in Table 8 reveal the reliability of the GSCD. Tables 4, 5, 6 and 7 show the obtained results. The top three measures for each assessment measure are highlighted in bold. We can see that the top methods are mostly GSCD, GMSD, FSIM and MAD. GSCD correlates much better with the subjective results than the other measures. Looking at the curves (Fig.2), the GSCD values are very close to DMOS and MOS, proving the efficiency of this measure.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Scatter plots of subjective scores versus scores from the proposed scheme on IQA databases</figcaption></figure>
Moreover, an interesting result is obtained from the comparison of the GSCD with GMSD, FSIM and MAD in Tables 5 (TID2008 database). The values of ROOC are close to 1; this means that GSCD has a similar performance as the methods or earlier works. Results clearly indicate that our GSCD measure performs quite well and is competitive with other IQA measures.

In addition, to compare the efficiency of different models, the average execution time required an image of size 512×384 is calculated (the image is taken from TID2008 database). All metrics were run on a TOSHIBA Satetillete T130-11U notebook with Intel Core U4100 CPU@1.30 GHz and 3G RAM. The software platform used to run all metrics was MATLAB R2007a (7.4.0). Table 8 shows the required time in seconds per image. It is shown in Table 9 that the proposed measure takes more time than the PSNR, the GMSD, and the SSIM and it is faster than the Fsim.

VIF, VSNR, IFC, MS-SSIM, GSM, MAD, DCTex, NQM and ERDDM also take much longer processing time than the proposed method.

Moreover, we adjusted the parameters based on a dataset of TID2008 database. The adjusting measure was that the parameters values giving to a higher ROOC would be chosen. As a result, the parameters required in the proposed method were set as: C₁= 100, C₂=2050.

<table><caption>**Table 3.** Four databases and their characteristics</caption><thead><tr><th>Database</th><th>Source Images</th><th>Distorted Images</th><th>Distortion Types</th><th>Image Type</th><th>Observers</th></tr></thead><tbody><tr><td>TID2008</td><td>25</td><td>1700</td><td>17</td><td>color</td><td>838</td></tr><tr><td>CSIQ</td><td>30</td><td>866</td><td>6</td><td>color</td><td>35</td></tr><tr><td>LIVE</td><td>29</td><td>779</td><td>5</td><td>color</td><td>161</td></tr><tr><td>TID2013</td><td>25</td><td>3000</td><td>25</td><td>color</td><td>971</td></tr></tbody></table>

<table><caption>**Table 4.** Performance comparison for image quality assessment measures on live database</caption><thead><tr><th>Method</th><th>ROCC</th><th>KROCC</th><th>CC</th><th>RMSE</th></tr></thead><tbody><tr><td>PSNR</td><td>0.8756</td><td>0.6865</td><td>0.8723</td><td>13.3597</td></tr><tr><td>SSIM</td><td>0.9479</td><td>0.7963</td><td>0.9449</td><td>8.9454</td></tr><tr><td>MS-SSIM</td><td>0.9513</td><td>0.8044</td><td>0.9409</td><td>9.2593</td></tr><tr><td>VSNR</td><td>0.9280</td><td>0.7625</td><td>0.9237</td><td>10.4694</td></tr><tr><td>VIF</td><td><strong>0.9632</strong></td><td><strong>0.8270</strong></td><td><strong>0.9598</strong></td><td><strong>7.6670</strong></td></tr><tr><td>IFC</td><td>0.9259</td><td>0.7579</td><td>0.9268</td><td>10.2643</td></tr><tr><td>NQM</td><td>0.9086</td><td>0.7413</td><td>0.9122</td><td>11.1926</td></tr><tr><td>ERDDM</td><td>0.9496</td><td>0.8128</td><td>0.9619</td><td>6.3204</td></tr><tr><td>DCTex</td><td>0.9483</td><td>0.8066</td><td>0.9443</td><td>8.9897</td></tr><tr><td>GSM</td><td>0.9554</td><td>0.8131</td><td>0.9437</td><td>9.0376</td></tr><tr><td>MAD</td><td><strong>0.9669</strong></td><td><strong>0.8421</strong></td><td><strong>0.9674</strong></td><td><strong>6.9235</strong></td></tr><tr><td>Fsim</td><td><strong>0.9645</strong></td><td><strong>0.8363</strong></td><td><strong>0.9613</strong></td><td><strong>7.5296</strong></td></tr><tr><td>GMSD</td><td>0.9603</td><td>0.8271</td><td>0.9603</td><td>7.622</td></tr><tr><td>GSCD</td><td>0.9596</td><td>0.8222</td><td>0.9538</td><td>8.2074</td></tr></tbody></table>
<table><caption>**Table 5.** Performance comparison for image quality assessment measures on TID2008 database</caption><thead><tr><th>Method</th><th>ROCC</th><th>KROCC</th><th>CC</th><th>RMSE</th></tr></thead><tbody><tr><td>PSNR</td><td>0.5794</td><td>0.4210</td><td>0.5726</td><td>1.1003</td></tr><tr><td>SSIM</td><td>0.7749</td><td>0.5768</td><td>0.7710</td><td>0.8546</td></tr><tr><td>MS-SSIM</td><td>0.8542</td><td>0.6568</td><td>0.8451</td><td>0.7173</td></tr><tr><td>VSNR</td><td>0.7049</td><td>0.5345</td><td>0.6823</td><td>0.9810</td></tr><tr><td>VIF</td><td>0.7496</td><td>0.5868</td><td>0.8090</td><td>0.7888</td></tr><tr><td>IFC</td><td>0.5675</td><td>0.4236</td><td>0.7340</td><td>0.9113</td></tr><tr><td>NQM</td><td>0.6243</td><td>0.4608</td><td>0.6142</td><td>1.0590</td></tr><tr><td>ERDDM</td><td>0.5961</td><td>0.4411</td><td>0.6685</td><td>0.998</td></tr><tr><td>DCTex</td><td>0.4973</td><td>0.4095</td><td>0.5605</td><td>1.1113</td></tr><tr><td>GSM</td><td>0.8554</td><td>0.6651</td><td>0.8462</td><td>0.7151</td></tr><tr><td>MAD</td><td>0.8340</td><td>0.6445</td><td>0.8306</td><td>0.7474</td></tr><tr><td>Fsim</td><td><strong>0.8840</strong></td><td><strong>0.6991</strong></td><td><strong>0.8762</strong></td><td><strong>0.6468</strong></td></tr><tr><td>GMSD</td><td><strong>0.8907</strong></td><td><strong>0.7094</strong></td><td><strong>0.8788</strong></td><td><strong>0.6404</strong></td></tr><tr><td>GSCD</td><td><strong>0.9000</strong></td><td><strong>0.7175</strong></td><td><strong>0.8830</strong></td><td><strong>0.629</strong></td></tr></tbody></table>

<table><caption>**Table 6.** Performance comparison for image quality assessment measures on TID2013 database</caption><thead><tr><th>Method</th><th>ROCC</th><th>KROCC</th><th>CC</th><th>RMSE</th></tr></thead><tbody><tr><td>PSNR</td><td>0.6396</td><td>0.4698</td><td>0.669</td><td>0.9214</td></tr><tr><td>SSIM</td><td>0.7417</td><td>0.5588</td><td>0.7895</td><td>0.7608</td></tr><tr><td>MS-SSIM</td><td>0.7859</td><td>0.6047</td><td>0.8329</td><td>0.6861</td></tr><tr><td>VSNR</td><td>0.6812</td><td>0.5084</td><td>0.7402</td><td>0.8392</td></tr><tr><td>VIF</td><td>0.6769</td><td>0.5147</td><td>0.7720</td><td>0.7880</td></tr><tr><td>IFC</td><td>0.5389</td><td>0.3939</td><td>0.5538</td><td>1.0322</td></tr><tr><td>NQM</td><td>0.6432</td><td>0.474</td><td>0.6858</td><td>0.9023</td></tr><tr><td>ERDDM</td><td>0.5623</td><td>0.4124</td><td>0.6352</td><td>1.230</td></tr><tr><td>DCTex</td><td>0.5863</td><td>0.4573</td><td>0.6495</td><td>0.9425</td></tr><tr><td>GSM</td><td>0.7946</td><td>0.6255</td><td>0.8464</td><td>0.6603</td></tr><tr><td>MAD</td><td>0.7807</td><td>0.6035</td><td>0.8267</td><td>0.6975</td></tr><tr><td>Fsim</td><td><strong>0.8510</strong></td><td><strong>0.6665</strong></td><td><strong>0.8769</strong></td><td><strong>0.5959</strong></td></tr><tr><td>GMSD</td><td><strong>0.8044</strong></td><td><strong>0.6343</strong></td><td><strong>0.859</strong></td><td><strong>0.6346</strong></td></tr><tr><td>GSCD</td><td><strong>0.8681</strong></td><td><strong>0.6855</strong></td><td><strong>0.8819</strong></td><td><strong>0.5844</strong></td></tr></tbody></table>
<table><caption>**Table 7.** Performance comparison for image quality assessment measures on CSIQ database</caption><thead><tr><th>Method</th><th>ROCC</th><th>KROCC</th><th>CC</th><th>RMSE</th></tr></thead><tbody><tr><td>PSNR</td><td>0.8005</td><td>0.5984</td><td>0.7998</td><td>0.1576</td></tr><tr><td>SSIM</td><td>0.8756</td><td>0.6907</td><td>0.8612</td><td>0.1334</td></tr><tr><td>MS-SSIM</td><td>0.9133</td><td>0.7393</td><td>0.8990</td><td>0.1150</td></tr><tr><td>VSNR</td><td>0.8104</td><td>0.6237</td><td>0.7993</td><td>0.1578</td></tr><tr><td>VIF</td><td>0.9195</td><td>0.7537</td><td>0.9277</td><td>0.0980</td></tr><tr><td>IFC</td><td>0.7671</td><td>0.5897</td><td>0.8384</td><td>0.1431</td></tr><tr><td>NQM</td><td>0.7402</td><td>0.5638</td><td>0.7433</td><td>0.1756</td></tr><tr><td>ERDDM</td><td>0.8626</td><td>0.6781</td><td>0.8295</td><td>0.1466</td></tr><tr><td>DCTex</td><td>0.8042</td><td>0.6420</td><td>0.7915</td><td>0.1605</td></tr><tr><td>GSM</td><td>0.9126</td><td>0.7403</td><td>0.8979</td><td>0.1156</td></tr><tr><td>MAD</td><td><strong>0.9467</strong></td><td><strong>0.7970</strong></td><td><strong>0.9502</strong></td><td><strong>0.0818</strong></td></tr><tr><td>Fsim</td><td>0.9310</td><td>0.7690</td><td>0.9192</td><td>0.1034</td></tr><tr><td>GMSD</td><td><strong>0.957</strong></td><td><strong>0.8133</strong></td><td><strong>0.9541</strong></td><td><strong>0.0786</strong></td></tr><tr><td>GSCD</td><td><strong>0.9602</strong></td><td><strong>0.8194</strong></td><td><strong>0.9578</strong></td><td><strong>0.0755</strong></td></tr></tbody></table>

<table><caption>**Table 8.** Ranking of IQA metrics' performance on four databases</caption><thead><tr><th>Method</th><th>Live</th><th>TID2008</th><th>TID2013</th><th>CSIQ</th></tr></thead><tbody><tr><td>PSNR</td><td>14</td><td>12</td><td>11</td><td>12</td></tr><tr><td>SSIM</td><td>10</td><td>7</td><td>7</td><td>8</td></tr><tr><td>MS-SSIM</td><td>7</td><td>5</td><td>5</td><td>6</td></tr><tr><td>VSNR</td><td>11</td><td>9</td><td>8</td><td>10</td></tr><tr><td>VIF</td><td><strong>2</strong></td><td>8</td><td>9</td><td>5</td></tr><tr><td>IFC</td><td>12</td><td>13</td><td>14</td><td>13</td></tr><tr><td>NQM</td><td>13</td><td>10</td><td>10</td><td>14</td></tr><tr><td>ERDDM</td><td>8</td><td>11</td><td>13</td><td>9</td></tr><tr><td>DCTex</td><td>9</td><td>14</td><td>12</td><td>11</td></tr><tr><td>GSM</td><td>6</td><td>4</td><td>4</td><td>7</td></tr><tr><td>MAD</td><td><strong>1</strong></td><td>6</td><td>6</td><td><strong>3</strong></td></tr><tr><td>Fsim</td><td><strong>3</strong></td><td><strong>3</strong></td><td><strong>2</strong></td><td>4</td></tr><tr><td>GMSD</td><td>4</td><td><strong>2</strong></td><td><strong>3</strong></td><td><strong>2</strong></td></tr><tr><td>GSCD</td><td><strong>5</strong></td><td><strong>1</strong></td><td><strong>1</strong></td><td><strong>1</strong></td></tr></tbody></table>
<table><caption>**Table 9.** Running time of the competing IQA models</caption><thead><tr><td>Method</td><td>Time (second)</td><td>Method</td><td>Time (second)</td></tr></thead><tbody><tr><td>PSNR</td><td>0.0493</td><td>ERDDM</td><td>9.6089</td></tr><tr><td>SSIM</td><td>0.1917</td><td>DCTex</td><td>0.5327</td></tr><tr><td>MS-SSIM</td><td>1.1304</td><td>GSM</td><td>1.4003</td></tr><tr><td>VSNR</td><td>1.5018</td><td>MAD</td><td>15.6235</td></tr><tr><td>VIF</td><td>5.1429</td><td>Fsim</td><td>2.4990</td></tr><tr><td>IFC</td><td>4.6738</td><td>GMSD</td><td>0.1602</td></tr><tr><td>NQM</td><td>1.8846</td><td>GSCD</td><td>0.4361</td></tr></tbody></table>

# 4 Conclusion

This paper describes an efficient method for image quality assessment. Its main feature is that this new method uses the gradient similarity and color distorted measure. The reference and test images are transformed respectively using color distorted and gradient mask. The difference between the reference and test images is computed using simple function. A comparative study has been carried in this work.

The obtained results are competitive with the previous works.

Future works following this study will include the use of others characteristics to assess image quality.

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