# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 1 Introduction  
Image quality is a basic concept in many image processing and computer vision applications, such as acquisition, transmission, and display. With advances in information technology and visual communication, assessing image quality has become a fundamental and challenging problem. Image Quality Assessment (IQA) automatically measures the image visual quality by effective computational models [1]. The image quality assessment (IQA) approach attempts to estimate the image quality based on human visual perception in an objective manner. Most IQA models based on full reference (FR) and achieved very good results, while most no reference (NR) IQA methods are designed for some predefined specific distortion types. Reduced reference (RR) IQA algorithms provide a proper compromise between FR and NR approaches, and they estimate the image quality with limited access to the reference image [2]. Recently, multimedia retargeting has attracted much attention in a graph and vision research. Retargeting techniques for image and video utilize the original visual scene to display that scene at different sizes or aspect ratios on different display screens. Many retargeting models have been proposed, such as multi-operator (MO), cropping (CR), streaming video (SV), shift-map (SM), seam carving (SC), scaling (SCL), scale-and-stretch (SNS), and warping (WARP) [3]. These models are either pixel-based or patch-based. Nevertheless, the FR image is not often available in time of retargeted IQA, but the reference image may be briefly described through partial information. Thus, an RR quality assessment criterionis needed to employ. Therefore, how to effectually assess the quality of the RR retargeted images is an important and challenging problem. However, general IQA approaches, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are not applied to the retargeted images because those algorithms require the coupled images have the same sizes. The existing retargeted IQA algorithms mainly based on full reference, such as color layout descriptor (CL), edge histogram (EH), bidirectional warping (BDW), bidirectional similarity (BDS), and scale invariant feature transform (SIFT) Flow [3]. Those algorithms have been used to evaluate retargeted images quality, although their results are not always consistent with subjective image evaluations. Since humans are the final evaluators of image quality, the goal must be to develop an automatic method that can evaluate image quality in a way that will be in agreement with subjective evaluation. RR algorithms often assess image quality by comparing the same features between reference and retargeted images, such as discrete cosine transform (DCT) [4], visual information fidelity (VIF) [5], divisive normalization transformation (DNT) [6], reduced reference entropic difference (RRED) [7], and reduced-reference SSIM (RRSSIM) [8]. [PERSON] et al. [9] proposed Gaussian scale mixture models to assess a RR image by computing the weighted entropies between reference and retargeted images. According to different types and levels of degradation, which could strongly influence saliency detection, a saliency-induced RR IQA method was introduced by [PERSON] et al. [10]. However, when the image is retargeted, some features may also change, so it is hard to compare our proposed algorithm with them directly. Thus, this paper proposes a useful method to estimate the visual quality for RR retargeted images. Since the size of the reference image is inconsistent with that of the retargeted image, this paper extracts the corresponding feature points of the two images and calculates the EMD between the image blocks corresponding to the feature points as local information. Meanwhile, this paper extracts the visual saliency features of the two images and calculates the EMD between the corresponding features as global information. Finally, local and global information are combined to generate a final quality score.