Abstract
Digital images have been extensively used in education, research, and entertainment. Many of these images, taken by consumer cameras, are compressed by the JPEG algorithm for effective storage and transmission. Blocking artifact is a well-known problem caused by this algorithm. Effective measurement of blocking artifacts plays an important role in the design, optimization, and evaluation of image compression algorithms. In this paper, we propose a no-reference objective blockiness measure, which is adaptive to high frequency component in an image. Difference of entropies across blocks and variation of block boundary pixel values in edge images are adopted to calculate the blockiness level in areas with low and high frequency component, respectively. Extensive experimental results prove that the proposed measure is effective and stable across a wide variety of images. It is robust to image noise and can be used for real-world image quality monitoring and control. Index Terms—JPEG, no-reference, blockiness measure
Highlights
Digital images provide great convenience to our daily life, storing and transmitting these images are problematic
We propose a no-reference objective blockiness measure, which is adaptive to high frequency component in an image
We propose a no-reference blockiness measure which is adaptive to different areas in an image
Summary
Digital images provide great convenience to our daily life, storing and transmitting these images are problematic. Consumer cameras can produce images with size more than 12 mega pixels. The standard JPEG algorithm installed in consumer cameras are commonly used to compress these images. The DCT-coded images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. It can dramatically degrade the image quality. Effective measurement of blocking artifacts plays an important role in the development and evaluation of these algorithms. We propose a no-reference adaptive blockiness measure for JPEG compressed images. For areas with high frequency component, we use the variation of block boundary pixel values in edge images as a measure.
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