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Save to Library. Create Alert. Share This Paper. Figures and Tables from this paper. Figures and Tables. Citations Publications citing this paper. Performance investigation on bilateral filter with confidence based over spatial correlation-based optical flow for image reconstruction Darun Kesrarat , Vorapoj Patanavijit.

Experimental efficiency analysis in robust models of spatial correlation optical flow methods under non Gaussian noisy contamination Darun Kesrarat , Vorapoj Patanavijit. Investigation of performance trade off in High Reliability and Robust Gradient Orientation on differential sub -pixel displacement optical flow algorithms over Non-Gaussian noisy model Darun Kesrarat , Paitoon Porntrakoon , Vorapoj Patanavijit.

Digital image processing

A robust optical flow estimation technique using gradient orientations Pramuk Boonsieng , Toshiaki Kondo , Waree Kongprawechnon. Gradient orientation pattern matching with the Hamming distance Toshiaki Kondo. Automatic movie analysis and summarisation Philip John Gorinski. References Publications referenced by this paper. Error control and concealment for video transmission using data hiding Marco Carli , Donald G. PSNR is the ratio between the maximum power of a signal and the power of corrupting noise that affects the fidelity of its representation.

The impact of image loss on positioning accuracy was assessed using a clinical image registration application — offline review Varian medical system, Palo Alto, CA, USA.

First, the original CBCT images were automatically registered with planning CT to determine target offset for patient positioning. The difference between both sets of target offsets is the discrepancy caused by image loss due to compression algorithm. This discrepancy represents the inconsistency of registration accuracy before and after compression. The image registration was performed automatically and the parameters were set for bony structures and soft tissues, respectively, as shown in dialog window of Figs.

Specifically, the intensity ranges for bony structure and soft tissues were set to 0— and — Compression ratio and running time are reported for three algorithms. For MJ2, there is no image loss due to the nature of lossless compression algorithm.

The running time is nearly constant with respect to different video decompression algorithms and treatment sites. Among three treatment sites, MSE is highest for HN cases, medium for thorax cases, and lowest for pelvis cases. VQM is highest for HN cases, medium for pelvis cases, and lowest for thorax cases. The columns of bone or soft tissues represent the result of registration based on bony structures or soft tissues as shown in Figs.

For two lossy compression algorithms, AVI and MP4, their mean values and standard deviations of discrepancies are similar and smaller. This is attributed to its algorithm in reducing more redundant information between two successive images. For different treatment sites, the compression ratios are varied to certain degrees. For those cases with larger variation between different sessions, the compression ratio using video compression algorithm might be lower. But if taking into account the amount of bits of a pixel, this error is smaller. Also note that the MP4 function provided by Matlab is based on H.

Its successor, H. Although there are image losses due to video compression algorithms, its impact on positioning accuracy is hardly discernible with respect to image sequence types, image intensity ranges and compression algorithms.

A framework of adaptive steganography resisting JPEG compression and detection

Since both lossy video compression algorithms demonstrated similar positioning accuracy with decompressed CBCT images, it is favorable to use MP4 as it provided higher compression ratio. Considering the time cost of video compression, AVI takes the most time while MP4 uses the least time. This may be caused by the variation of computation times of three video compression algorithms.

For decompression process, the running times of three video compression algorithms are close. Although the running time of video compression algorithm is varied, the absolute value of running time is smaller. In addition to the higher compression ratio, there is another advantage in compressing CBCT using video compression algorithm, i. The common image compression algorithm placed all images in a folder and generated a zip file for all images in the folder. The image content in the zip file could not be viewed unless it was unzipped.

With video compression algorithm, the resulting movie file can be viewed freely without decompression process. This is a huge benefit for many medical image applications. Video compression method is an effective way for the repository of clinical CBCT data.

Image compression - Wikipedia

The lossless video compression method provides lower compression ratio but higher image quality, while the lossy compression method provides higher compression ratio but lower image quality. For patient positioning, MP4 is the most suitable method for CBCT image compression among three video encoders because it has highest compression ratio and comparable positioning accuracy. As video compression methods may cause image loss, it should be cautious to apply them for those clinical applications which required higher quality of image detail. Volume 20 , Issue 9. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account.

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ISBN 13: 9780849315268

Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Materials and methods At first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Results Among three video compression algorithms, Motion JPEG has the least compression ratio since it is a lossless compression algorithm.

Conclusions Video compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Figure 1 Open in figure viewer PowerPoint. Figure 2 Open in figure viewer PowerPoint. Figure 3 Open in figure viewer PowerPoint. Figure 4 Open in figure viewer PowerPoint.

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