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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Atallah AM, Ali HS, Abdallsh MI, 2016. An integrated system for underwater wireless image transmission. 28th Int Conf on Microelectronics, p.169–172. https://doi.org/10.1109/ICM.2016.7847936
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. https://doi.org/10.1109/TIT.2005.862083
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. https://doi.org/10.1109/ICSPCC.2016.7753722
Donoho DL, 2006. Compressed sensing. IEEE Trans Infrom Theory, 52(4):1289–1306. https://doi.org/10.1109/tit.2006.871582
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. https://doi.org/10.1007/s11042-007-0111-1
Kourzi A, Nuzillard D, Millon G, et al., 2005. Quality estimation in wavelet image coding. Proc 13th European Signal Processing Conf, p.1–4.
Liu A, Lin W, Narwaria M, 2012. Image quality assessment based on gradient similarity. IEEE Trans Image Process, 21(4):1500–1512. https://doi.org/10.1109/TIP.2011.2175935
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.
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. https://doi.org/10.1109/97.841153
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. https://doi.org/10.1109/76.499834
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. https://doi.org/10.5120/1158-1434
Sheikh HR, Bovik AC, 2006. Image information and visual quality. IEEE Trans Image Process, 15(2):430–444. https://doi.org/10.1109/TIP.2005.859378
Sophia PE, Anitha J, 2016. Region-Based Prediction and Quality Measurements for Medical Image Compression. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_29
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. https://doi.org/10.1109/TIE.2017.2698406
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. https://doi.org/10.1007/978-3-319-47274-4_4
Wang Z, Bovik AC, 2002. A universal image quality index. IEEE Signal Process Lett, 9(3):81–84. https://doi.org/10.1109/97.995823
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. https://doi.org/10.1109/ACSSC.2003.1292216
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. https://doi.org/10.1109/TIP.2003.819861
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. https://doi.org/10.1109/TIP.2013.2293423
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. https://doi.org/10.1007/s11045-015-0333-8
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. https://doi.org/10.1109/TIP.2011.2109730
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 61571377, 61771412, and 68713367) and the Fundamental Research Funds for the Central Universities, China (No. 20720180068)
Rights and permissions
About this article
Cite this article
Cai, Yq., Zou, Hx. & Yuan, F. Adaptive compression method for underwater images based on perceived quality estimation. Frontiers Inf Technol Electronic Eng 20, 716–730 (2019). https://doi.org/10.1631/FITEE.1700737
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1700737
Key words
- Underwater image compression
- Set partitioning in hierarchical trees
- Compressive sensing
- Compression quality estimation