CLC number: TP391.41
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-03-27
Cited: 0
Clicked: 6329
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.
@article{title="Adaptive compression method for underwater images based on perceived quality estimation",
author="Ya-qiong Cai, Hai-xia Zou, Fei Yuan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="5",
pages="716-730",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700737"
}
%0 Journal Article
%T Adaptive compression method for underwater images based on perceived quality estimation
%A Ya-qiong Cai
%A Hai-xia Zou
%A Fei Yuan
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 5
%P 716-730
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700737
TY - JOUR
T1 - Adaptive compression method for underwater images based on perceived quality estimation
A1 - Ya-qiong Cai
A1 - Hai-xia Zou
A1 - Fei Yuan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 5
SP - 716
EP - 730
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700737
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.
[1]Atallah AM, Ali HS, Abdallsh MI, 2016. An integrated system for underwater wireless image transmission. 28th Int Conf on Microelectronics, p.169-172.
[2]Candès EJ, Romberg J, Tao T, 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory, 52(2):489-509.
[3]Chen WL, Yuan F, Cheng E, 2016. Adaptive underwater image compression with high robust based on compressed sensing. IEEE Int Conf on Signal Processing, p.1-6.
[4]Donoho DL, 2006. Compressed sensing. IEEE Trans Infrom Theory, 52(4):1289-1306.
[5]Koumaras H, Kourtis A, Martakos D, et al., 2007. Quantified PQoS assessment based on fast estimation of the spatial and temporal activity level. Multim Tools Appl, 34(3):355-374.
[6]Kourzi A, Nuzillard D, Millon G, et al., 2005. Quality estimation in wavelet image coding. Proc 13th European Signal Processing Conf, p.1-4.
[7]Liu A, Lin W, Narwaria M, 2012. Image quality assessment based on gradient similarity. IEEE Trans Image Process, 21(4):1500-1512.
[8]Ponomarenko N, Silvestri F, Egiazarian K, et al., 2007. On between-coefficient contrast masking of DCT basis functions. 3rd Int Workshop on Video Processing and Quality Metrics, p.1-4.
[9]Saha S, Vemuri R, 2002. An analysis on the effect of image features on lossy coding performance. IEEE Signal Process Lett, 7(5):104-107.
[10]Said A, Pearlman WA, 1996. A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circu Syst Video Technol, 6(3):243-250.
[11]Sarita K, Meel VS, Ritu V, 2011. Image quality prediction by minimum entropy calculation for various filter banks. Int J Comput Appl, 7(5):31-34.
[12]Sheikh HR, Bovik AC, 2006. Image information and visual quality. IEEE Trans Image Process, 15(2):430-444.
[13]Sophia PE, Anitha J, 2016. Region-Based Prediction and Quality Measurements for Medical Image Compression. Springer, Singapore.
[14]Tang CQ, Tian GY, Li KJ, et al., 2017. Smart compressed sensing for online evaluation of CFRP structure integrity. IEEE Trans Ind Electron, 64(12):9608-9617.
[15]Tichonov J, Kurasova O, Filatovas E, 2016. Quality prediction of compressed images via classification. 8th Int Conf on Image Processing and Communications Challenges, p.35-42.
[16]Wang Z, Bovik AC, 2002. A universal image quality index. IEEE Signal Process Lett, 9(3):81-84.
[17]Wang Z, Simoncelli EP, Bovik AC, 2003. Multiscale structural similarity for image quality assessment. 37th Asilomar Conf on Signals, Systems and Computers, p.1398-1402.
[18]Wang Z, Bovik AC, Sheikh H, et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process, 13(4):600-612.
[19]Xue WF, Zhang L, Mou XQ, et al., 2014. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process, 23(2):684-695.
[20]Zemliachenko A, Lukin V, Ponomarenko N, et al., 2016. Still image/video frame lossy compression providing a desired visual quality. Multidimens Syst Signal Process, 27(3):697-718.
[21]Zhang L, Zhang L, Mou X, et al., 2011. FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process, 20(8):2378.
Open peer comments: Debate/Discuss/Question/Opinion
<1>