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A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms

机译：利用人工神经网络的图像压缩算法感知图像质量评估模型

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The demand of an accurate objective image quality assessment tool is important in modern multimedia systems. Image coding algorithms introduce highly structured coding artifacts and distortions. In this paper, we present a novel approach to predict the perceived image quality. Properties of the Human Visual System (HVS) were exploited to select a set of suitable metrics. These metrics are extracted while comparing the reference and distorted image. Mutual Information (MI) and Principal Component Analysis (PCA) were used to obtain an optimal set of objective features that best describe the perceived image quality in respect to subjective scores from human observers. The impairment feature vector is forwarded to the Artificial Neural Network (ANN) where the features are combined and the predicted quality score is computed. Parameters of the ANN are adjusted using Mean Opinion Scores (MOS) obtained from the group of assessors. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings. Possible incorporation of the model into a perceptual image-coding algorithm is proposed. Such a system is capable to ensure that only visually important information is encoded and consequently that the required communication bandwidth is minimized.
机译：在现代多媒体系统中，对准确的客观图像质量评估工具的需求非常重要。图像编码算法引入了高度结构化的编码伪像和失真。在本文中，我们提出了一种预测感知图像质量的新颖方法。利用人类视觉系统（HVS）的属性来选择一组合适的指标。在比较参考图像和失真图像时提取这些度量。互信息（MI）和主成分分析（PCA）用于获得一组最佳的客观特征，这些特征最好地描述了来自人类观察者的关于主观得分的感知图像质量。损伤特征向量被转发到人工神经网络（ANN），在此将特征进行组合并计算预测的质量得分。使用从评估者组获得的平均意见评分（MOS）调整ANN的参数。结果表明，所提出的图像质量评估模型可以与主观图像质量等级实现高度相关。提出了将模型合并到感知图像编码算法中的建议。这样的系统能够确保仅对视觉上重要的信息进行编码，从而使所需的通信带宽最小化。

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