Side Scan Sonar Image Compression

Abstract

Wireless communication between underwater vehicles such as side scan sonar (SSS) and its operator is crucial for perceiving correct and updated intelligence understanding of the seabed. This has many military applications such as underwater mine discovery, and civilian applications such as seabed texture analysis.

SSS images usually contain high resolution data, and have high frequency content. Hence, aren’t compressed well by simple compression schemes such as JPEG.
The Goal of this project is finding a compression algorithm that on the one hand manages to compress SSS with high compression ratios and low complexity, and on the other, preserve the images' features that has intelligence value.

Few compression schemes that specialize at high resolution image compression were examined, implemented and tested through the project. The best algorithm found, was compared to the JPEG 2000 standard as reference, by having subjective quality assessment tests, and comparing quality factors such as PSNR and SSIM.

Introduction

Communication between underwater vehicles and their operator is crucial for underwater communication. There are few underwater autonomic vehicles that manage to explore the seabed efficiently; one of them is the side scan sonar (SSS). SSS creates sonar mapping of the seabed and provides understanding of the differences in material and texture type of the seabed.

Data provided by the SSS, present large uniform areas disrupted by rocks, shipwrecks or pipelines and is inherently noisy. Therefore we must find effective ways for transmitting high resolution data should be found, to withstand the system’s limitation of bit rate and bandwidth.

     Figure 2 - SSS image patch

The goal of the project is designing an efficient compression scheme for SSS images that preserve the image's important details, achieves high compression ratios and have low complexity.
The solution
Classic image compression algorithms such as JPEG don’t work well on SSS images for its high resolution data, and high frequency content.
The approach we chose was wavelet based compression. Wavelets are a family of functions, obtained from a prototype function by scaling and translating.

Wavelet representation allows fine frequency analysis and good localization in time. Wavelets are useful multi resolution signal analysis tool. They enable studying different resolution layers of the image and help de-noising some of the speckles of SSS images.

Figure 3 – Wavelet Decomposition

We implemented in matlab several wavelet based compression schemes. The first one is based on sparse representation of the wavelet coefficients and Huffman coding:implemented and tested through the project. The best algorithm found, was compared to the JPEG 2000 standard as reference, by having subjective quality assessment tests, and comparing quality factors such as PSNR and SSIM.

Side Scan Sonar Image Compression

 

Sparse DWT compression scheme

Figure 4 – Sparse DWT compression scheme

Second approach we implemented is based on SPIHT algorithm. The method uses the hierarchal structure of the wavelet decomposition and smart representation of the data to send a lot of information for a little cost. Information is sent in bit planes and represented in large hierarchal zero trees.

Figure 5 - Hierarchal relations in DWT

Figure 5 – Hierarchal relations in DWT

Figure 6 – SPIHT based compression scheme

Figure 6 – SPIHT based compression scheme

 

Results

Sparsed DWT copmressed images

Figure 7 – Sparsed DWT copmressed images

Figure 8 - SPIHT compressed images

Figure 8 – SPIHT compressed images

Figure 9 - Compressed patches compared to JPEG and JPEG2000 as reference

Figure 9 – Compressed patches compared to JPEG and JPEG2000 as reference , Compresstion ratio = 20
Subjective Quality Assessment
SPIHT method was compared to JPEG and JPEG2000 using subjective quality evaluation – MOS (mean opinion score).
Sonar analysts ranked compressed images:
• 18 sets of images
• Each set contains 13 images compressed by JPEG, SPIHT, JPEG2000 and the original image.
• Sets were divided into 3 separate sessions.
• Specially costumed GUI was built.

Figure 10 - Subjective Quality Assessment GUI

Figure 10 – Subjective Quality Assessment GUI

Figure 11 - MOS Ranking

Figure 11 – MOS Ranking
Conclusions
• Several SSS image compression and coding algorithms have been implemented, tested and compared.
• There is a tradeoff between high compression ratios to image quality.
• MOS Results were not conclusive.
• Choice of compression algorithm is highly influenced by the application.
Further Research
• Improving algorithm robustness for transmission errors.
• Implementing the algorithm in an operational system – real-time implementation.

References
[1] Gregory K. Wallace, “The JPEG Still Picture Compression Standard”, IEEE Transactions on Consumer Electronics, 1991.
[2] R. A. Cunha, M. T. Figueiredo and C. J. Silvestre, “Simultaneous Compression and Denoising of Side Scan Sonar Images Using the Discrete Wavelet Transform”, OCEANS 2000 MTS/IEEE Confrence and Exhibition, 2000.
[3] Khalid Sayood, “Introduction to Data Compression” 4th edition pages 498-568, 2012.
[4] David S. Taubman, Michael W. Marcellin, “JPEG2000 : Image Compression Fundamentals Standards and Practice”, Kluwer Academic Publishers, 2002.

Collaboration:

Dr. Alon Amar,

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Project web page