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Summary:  <168>

CLC number: TP391.41

On-line Access: 2019-06-10

Received: 2017-11-09

Revision Accepted: 2018-09-13

Crosschecked: 2019-03-27

Cited: 0

Clicked: 1301

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei Yuan

https://orcid.org/0000-0002-8614-8756

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.5 P.716-730

http://doi.org/10.1631/FITEE.1700737


Adaptive compression method for underwater images based on perceived quality estimation


Author(s):  Ya-qiong Cai, Hai-xia Zou, Fei Yuan

Affiliation(s):  Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Ministry of Education, Xiamen University, Xiamen 361005, China

Corresponding email(s):   caiyaqiong@stu.xmu.edu.cn, 850605461@qq.com, yuanfei@xmu.edu.cn

Key Words:  Underwater image compression, Set partitioning in hierarchical trees, Compressive sensing, Compression quality estimation


Ya-qiong Cai, Hai-xia Zou, Fei Yuan. Adaptive compression method for underwater images based on perceived quality estimation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(5): 716-730.

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doi="10.1631/FITEE.1700737"
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Abstract: 
underwater image compression is an important and essential part of an underwater image transmission system. An assessment and prediction method of effectively compressed image quality can assist the system in adjusting its compression ratio during the image compression process, thereby improving the efficiency of the image transmission system. This study first estimates the perceived quality of underwater image compression based on embedded coding compression and compressive sensing, then builds a model based on the mapping between image activity measurement (IAM) and bits per pixel and structural similarity (BPP-SSIM) curves, next obtains model parameters by linear fitting, and finally predicts the perceived quality of the image compression method based on IAM, compression ratio, and compression strategy. Experimental results show that the model can effectively fit the quality curve of underwater image compression. According to the rules of parameters in this model, the perceived quality of underwater compressed images can be estimated within a small error range. The presented method can effectively estimate the perceived quality of underwater compressed images, balance the relationship between the compression ratio and compression quality, reduce the pressure on the data cache, and thus improve the efficiency of the underwater image communication system.

基于质量感知的水下图像自适应压缩方法

摘要:水下图像压缩是水声图像传输系统必不可少且至关重要的一个环节,有效预测感知压缩图像质量能使系统在压缩过程更好调整压缩率,提高图像传输通信系统效率。首先分别对压缩感知和嵌入式编码两种压缩策略下的水下压缩图像进行质量感知,然后利用图像活动性IAM(Image Activity Measurement)与BPP-SSIM(Bits Per Pixel and Structural SIMilarity)曲线间的映射建模并获得模型参数,从而根据图像的空域活动性、压缩率和压缩策略预测图像压缩质量。实验结果表明,所建立的模型能有效拟合水下图像压缩质量曲线,根据模型中参数具有的规律性,能够在较小误差范围内预测水下压缩图像的感知质量。所提方法能够有效预测感知水下图像压缩质量,并有效权衡压缩率与压缩质量之间的关系,减小发送端的数据缓存压力,提高水下图像通信系统效率。

关键词:水下图像压缩;SPIHT压缩;压缩感知;压缩质量预测

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