基于质量感知的水下图像自适应压缩方法（英文）
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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(ImageActivity 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。
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.
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.
引文
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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 13thEuropean 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.3rdInt 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.8thInt 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.IEEETrans Image Process,20(8):2378.https://doi.org/10.1109/TIP.2011.2109730