A No-Reference Image Quality Objective Evaluation Method Based on Deep Learning

Abstract

The invention discloses a non-reference image quality objective evaluation method based on deep learning. Multi-resolution pyramid and Gaussian difference decomposition is performed on distortion images to be evaluated and then natural statistical characteristics can be extracted by performing simple local normalization on sub-band images without extracting characteristics from a transform domain so that complexity is greatly reduced. Degree of distortion of the images is measured by degree of loss of the natural statistical characteristics with no requirement for reference images or distortion types. The change condition of visual quality of the images under the influence of various image processing and compression methods can be objectively reflected by the method, and evaluation performance of the method is not influenced by the image content or the distortion types and is consistent with subjective perception of human eyes. An existing L moment estimation method is adopted to estimate the distribution parameters of the envelope curve of a gray level histogram, and the distribution parameters obtained through estimation are more accurate and have higher generalization capability.

Description

technical field [0001] The present invention relates to an image quality evaluation method, in particular to an objective evaluation method of image quality without reference based on deep learning. Background technique [0002] Image quality assessment (IQA) is an integral part of many image processing applications. The objective image quality evaluation model is an algorithm that can automatically predict the degree of image distortion, and is usually used to monitor multimedia services to ensure that end users obtain satisfactory quality of experience. According to whether the original reference image is available, objective image quality assessment can usually be divided into three categories, namely, full reference image quality assessment, semi-reference image quality assessment, and blind image quality assessment (BIQA) without reference. The no-reference image quality assessment method, which can predict the perceived quality of an image without a reference image an...

AI Technical Summary

Problems solved by technology

[0003] Most of the existing NSS-based no-reference image quality assessment methods extract natural statistical features from different transform domains. For example, Moorthy et al. extracted NSS features from the wavelet domain, and proposed the blind image quality assessment (Blind ImageQuality Index, BIQI) and Its improved algorithm is Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE); Saad et al. proposed an improved blind image integrity evaluation based on DCT statistical properties in the DCT domain ( BLindImage Integrity Notator using DCT Statistics-II, BLIINDS-II); Liu et al. used local spatial domain and frequency domain entropy as features to propose a quality evaluation based on spatiotemporal domain entropy (Spatial–Spectral Entropy-based Quality index, SSEQ); however, a On the one hand, the time complexity of these methods is very high, which is not conducive to real-time applications; on the other hand, these methods require machine learning methods to train prediction models. Commonly used machine learning methods include neural networks, support vector bases, random forests, etc. However, Since these machine learning methods are shallow learning methods, usually composed of a single or double nonlinear feature transformation layer, these machine learning methods are not accurate enough in mapping features to real labels, and it is well known that the human visual mechanism is very Complex, difficult to be well represented by shallow learning methods

Hou et al. trained a deep belief network (DBN) classification model to predict image quality; Gu et al. extracted a large number of statistical features and trained a stacked auto-encoder (Stacked auto-encoder, SAE) with These methods are no-reference image quality evaluation methods based on deep learning, but the evaluation accuracy of these methods is low, indicating that the classic deep learning model cannot be directly applied to image quality evaluation

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