Image quality evaluation method based on independent component analysis

A technology of image quality evaluation and independent component analysis, applied in the field of image processing, can solve the problem of unable to analyze and process color images, and achieve the effect of improving execution efficiency

Inactive Publication Date: 2014-04-23
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] Aiming at the problem that the current image quality evaluation method based on independent component analysis cannot analyze a

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  • Image quality evaluation method based on independent component analysis
  • Image quality evaluation method based on independent component analysis
  • Image quality evaluation method based on independent component analysis

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Embodiment Construction

[0022] The present invention is not only applicable to color images, but also to grayscale images. The present invention will be further described below in combination with specific embodiments.

[0023] Assuming that the reference image set is R, the image to be evaluated D and the reference image O have the same number of color channels, and the width and height of the image to be evaluated D and the reference image O are W and H, respectively.

[0024] The flowchart of the method of the present invention is as figure 1 shown, including the following steps:

[0025] Step 1. Randomly segment image blocks from the reference image set and perform vectorization.

[0026] Step 1.1, randomly segment the reference image set R into N image blocks p of the same size through a sliding window of size k×k i (i=1,2,...,N) as the training data set T. Among them, each image block is a square with side length k, (k

[0027] Step 1.2, each image block p i vectorize to a column...

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Abstract

The invention relates to an image quality evaluation method based on independent component analysis. The image quality evaluation method is characterized by being suitable for image quality evaluation of a grey scale map and a color image synchronously. The image quality evaluation method comprises the following steps of firstly, centrally training a group of ICA (Independent Component Analysis) decomposing matrixes from a reference image by utilizing a FastICA (Fast Independent Component Analysis) algorithm; secondly, multiplying each image block in the reference image and an image to be evaluated, and the ICA decomposing matrixes so as to obtain the independent component of each image block; lastly, measuring the quality of the image to be evaluated according to the difference of the independent components of the reference image and the image to be evaluated. In comparison with the conventional method, the method is capable of simulating expression of a visual signal in a human visual cortex and is closer to subjective image quality evaluation. The main calculated quantity of the method is centralized to the independent components, which are obtained by multiplying each split image block and the ICA decomposing matrixes, of the image blocks, but the calculation of each image block is independent, so that parallel computing is adopted, thus the execution efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image quality evaluation method based on independent component analysis. Background technique [0002] Image quality is an important indicator for comparing the performance of various image processing algorithms and optimizing system parameters. Therefore, it is of great significance to establish an effective image quality evaluation mechanism in the fields of image acquisition, encoding compression, and network transmission. Image quality evaluation is widely used in all aspects of digital imaging systems (acquisition, compression, encoding, denoising, enhancement, watermarking, authentication, storage, synthesis and replication, etc.) to achieve real-time quality monitoring, parameter and performance optimization and other purposes. The traditional subjective image quality evaluation mainly relies on the measurement of psychological experiments, and these experiments ...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 段立娟席涛杨震马伟乔元华齐洪钢
Owner BEIJING UNIV OF TECH
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