Recently, image super-resolution has reinforced image resolution enhancement approaches in real-time and ensuring visual quality of super resolved images has evolved as a key research problem. Most quantitative benchmarks rely on full reference metrics which would work in the presence of a reference image. However, the unavailability of ground truth images in real world applications and the size constraints of low resolution and high resolution images often pose major challenges to such metrics. In order to address these problems, we present a super-resolution image quality index (SRQC –Super-resolution Quality Criterion), which can effectively quantify the efficiency and performance of image super-resolution algorithms. SRQC benchmark evaluates the quality score of a super resolved image according to the perceptual concepts of low-level spatial features in high sharpness space and curvelet based quality-aware features from focal energy bands, which can be used to capture the quality preservation of an SR image. The proposed metric is referenceless, the significance being that the assessment does not require ground-truth image. Explicitly, the SR image is assessed in the curvelet domain which is suitable for the no-reference super-resolution image quality assessment based on human perception. Experimental scores illustrate that the SRQC is more competent in modeling the features from curvelet transform, thus quantifying the quality score of the super resolved image and outperforming the formerly reported image quality assessment metrics.

近来，图像超分辨率已经实时增强了图像分辨率增强方法，并且确保超分辨图像的视觉质量已经发展成为关键的研究问题。大多数定量基准都依赖于完整的参考指标，这些参考指标将在存在参考图像的情况下起作用。然而，现实应用中地面真相图像的不可用以及低分辨率和高分辨率图像的尺寸限制通常给此类度量带来了重大挑战。为了解决这些问题，我们提出了一种超分辨率图像质量指标（SRQC –超分辨率质量标准），可以有效地量化图像超分辨率算法的效率和性能。SRQC基准根据高清晰度空间中低级空间特征和聚焦能带中基于Curvelet的质量感知特征的感知概念来评估超分辨图像的质量得分，可用于捕获SR的质量保存图片。所提出的度量标准没有参考价值，其意义在于该评估不需要真实的图像。明确地说，SR图像是在Curvelet域中评估的，它适合基于人的感知的无参考超分辨率图像质量评估。实验分数表明，SRQC在建模来自Curvelet变换的特征方面更胜任，因此可以量化超分辨图像的质量分数，并且胜过以前报告的图像质量评估指标。