An Underwater Color Image Quality Evaluation Metric

Abstract

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

Underwater image and video dehazing with pure haze region segmentation

“…To quantify performance we use UCIQE, Underwater Color Image Quality Evaluation ( Yang and Sowmya, 2015 ), as well as a subjective evaluation. UCIQE is an underwater-specific no-reference metric that takes into account the standard deviation of chroma σ χ , the contrast of lightness ψ L * and the mean of saturation μ s .…”

“…Saturation = χ L * and ψ L * is defined as the difference between the bottom 1% and the top 1% of pixels in the lightness channel L * of the CIE L * a * b * colour space. We carried out a subjective evaluation as a single-stimulus subjective test with 12 participants and multiple linear regression to determine the weighted coefficients ( ω 1 = 0.3921, ω 2 = 0.2853 and ω 3 = 0.3226) for the individual parts of the measure on the PKU-EAQA dataset of 100 unprocessed underwater images ( Chen et al, 2014;Yang and Sowmya, 2015 ). The subjective evaluation of the processed videos consists of a paired-stimulus approach where participants were shown two processed sequences of different methods and asked to decide which they preferred (if any).…”

“…UCIQE ( Yang and Sowmya, 2015 ) Table 3 Quantitative results for the method of Ancuti et al (2012) ( Fig. 10 (b)) and the proposed method ( Fig.…”

“…10 (c)) for images 1, 2, 40 and 47 of the PKU-EAQA dataset ( Chen et al, 2014 ). Individual parts of UCIQE ( Yang and Sowmya, 2015 ) Wang, 2010;Wen et al, 2013 ) and a histogram adjustment method ( Chen et al, 2014 ). We use the same parameter settings and follow the same paired preference experiment detailed in the previous section.…”

“…A patch size of 9 × 9 pixels is used for the generation of the dark channel in all of the methods. Table 4 shows quantitative results of the three methods with the UCIQE metric ( Yang and Sowmya, 2015 ) previously described in Section 6.1 for 10 underwater images previously used to evaluate underwater enhancement methods ( Emberton et al, 2015 ). Although quantitative measures are not always reliable these results suggest an improved performance for the proposed method.…”