JPEG2000 Still Image Coding Quality
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JPEG2000 Still Image Coding Quality Tzong-Jer Chen & Sheng-Chieh Lin & You-Chen Lin & Ren-Gui Cheng & Li-Hui Lin & Wei Wu
Published online: 16 April 2013 # Society for Imaging Informatics in Medicine 2013
Abstract This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r>0.98, p25), the variable approximately follows a normal distribution with the mean and variance given by [24] a ¼ 1=ðN 1Þ and
N ðN 2 3N þ 3ÞS1 NS2 þ 3S02 K N ðN 1ÞS 1 2NS2 þ 6S02 a2 ðN 1ÞðN 2ÞðN 3ÞS02
where K is defined by
K¼N
X
4
ð xi x Þ =
hX
ðxi xÞ
2
i2
ð8Þ
ð6Þ
ð7Þ
certain areas to form a peak [7, 22]. The MPR is a peak ratio of the Z value between manipulated and original images. It has been proven to correspond well to the image variation in spatial properties. The higher the Q and the lower the MPR correlate well with better image quality. The HVS-Based Metric
where S1=2S0 and S1=8(8mn-7m-7n+4). The standardized normal statistic Z¼
Ca σ
ð9Þ
can be used to determine the structural information in an image [7, 21, 22]. Collecting all Z values in an image and then sorting them into bins can produce a Z histogram. The spatial correlation increases with the amount of image blurring and accompanies the increase in Z value. This Z value will increase in
Because a human observer is the end user of image quality measurement, an image quality model based on HVS seems to be more appropriate for user perception. In order to obtain a closer relation with the assessment by the human visual system, both the original and compressed images can be preprocessed via filters that simulate the HVS. The models for the human visual system are, in general, given as a bandpass filter with a transfer function in polar coordinates [13].
K ðρÞ ¼
n
ρ0.98, p
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