Image and video compression: A survey

Clarke, R.J.

doi:10.1002/(sici)1098-1098(1999)10:1<20::aid-ima3>3.0.co;2-4

Cited by 13 publications

(10 citation statements)

References 56 publications

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“…With these systems, it is difficult to exploit hardware acceleration that is essential for our biomedical image processing. Furthermore, the compression techniques [13,60,10,40] employed in general video streaming may introduce artifacts because of their high compression rate, and such artifacts are not desirable for biomedical image applications.…”

Section: Updating Screensmentioning

confidence: 99%

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Communication Platform for Image Analysis and Sharing in Biology

Murakami

Tawara

Nishimura

et al. 2014

IJNC

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Image analysis is crucial to medical and biological applications. Recent advances in imaging technology have led to the demand for processing and visualizing a large amount of three-dimensional (3D) biomedical images. In addition, cloud computing has become popular for managing big data. Unfortunately, conventional image-processing systems either lack cloud computing services or advanced 3D processing abilities. In this paper, we present a novel cloud-based system for sharing, processing, and visualizing 3D biomedical images. Our system employs a standard web browser as a client interface that interactively communicates with high-performance servers. Thus, an inexpensive tablet PC without an advanced graphics processing unit (GPU) can be used for 3D image processing and visualization. Our system provides the sharing of limited software and hardware resources, and it allows for effective collaboration between researchers. We demonstrate the applicability and functionality of the system by examining typical case studies on biomedical images. We also examine the performance of our system numerically.

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Section: Updating Screensmentioning

confidence: 99%

“…Image and video compression techniques have been well studied [13,60,10,40]; however, the techniques for interactive applications have not been well investigated compared with the field of geometry compression.…”

Section: Quality Adjusted Screen Transfermentioning

confidence: 99%

Communication Platform for Image Analysis and Sharing in Biology

Murakami

Tawara

Nishimura

et al. 2014

IJNC

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“…Image data has its mean value removed and is then subjected to an 8x8 discrete cosine transform, followed by scaling and uniform quantization according to predefined tables. The mean value component is calculated block to block and the 63 other coefficients are then Huffman coded in amplitude and location along a zig-zag path running across the coefficient array from top left (DC term) to bottom right (highest frequencies) [3]. A disadvantage to this approach is that at low data rates block-structured artifacts appear in the reproduced image.…”

Section: Literature Surveymentioning

confidence: 99%

“…The 8x8 blocks are combined into macro-blocks consisting of four 8x8 luminance blocks. Motion compensation is carried out over 16x16 luminance blocks [3].…”

Section: Mpeg-imentioning

confidence: 99%

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Application specific compression : final report.

Melgaard¹,

Byrne²,

Myers³

et al. 2008

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With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% of the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3. 5

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