&lt;title&gt;Comparison of wavelet image coders using picture quality scale (PQS)&lt;/title&gt;

Lü, Jian; Algazi, V. Ralph; Estes, Robert R.

doi:10.1117/12.205371

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…Effect of quantization and coding on the compression ratio has been investigated by several investigators and will not be addressed in this study. 14,15 Selection of different types of methods for quantization and coding may change the compression ratio, but the general relationship between the compression ratio with masking and that without masking will remain unchanged. Selection of type of quantization and coding may also depend on the type of images to be compressed.…”

Section: Resultsmentioning

confidence: 99%

Oncologic image compression using both wavelet and masking techniques

Yin

Gao

1997

Medical Physics

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A new algorithm has been developed to compress oncologic images using both wavelet transform and field masking methods. A compactly supported wavelet transform is used to decompose the original image into high- and low-frequency subband images. The region-of-interest (ROI) inside an image, such as an irradiated field in an electronic portal image, is identified using an image segmentation technique and is then used to generate a mask. The wavelet transform coefficients outside the mask region are then ignored so that these coefficients can be efficiently coded to minimize the image redundancy. In this study, an adaptive uniform scalar quantization method and Huffman coding with a fixed code book are employed in subsequent compression procedures. Three types of typical oncologic images are tested for compression using this new algorithm: CT, MRI, and electronic portal images with 256 x 256 matrix size and 8-bit gray levels. Peak signal-to-noise ratio (PSNR) is used to evaluate the quality of reconstructed image. Effects of masking and image quality on compression ratio are illustrated. Compression ratios obtained using wavelet transform with and without masking for the same PSNR are compared for all types of images. The addition of masking shows an increase of compression ratio by a factor of greater than 1.5. The effect of masking on the compression ratio depends on image type and anatomical site. A compression ratio of greater than 5 can be achieved for a lossless compression of various oncologic images with respect to the region inside the mask. Examples of reconstructed images with compression ratio greater than 50 are shown.

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Section: Resultsmentioning

confidence: 99%

Oncologic image compression using both wavelet and masking techniques

Yin

Gao

1997

Medical Physics

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“…The optimal threshold limit that corresponds to 99.6% of retained energy is selected automatically. The recovered energy is calculated by using a secondorder vector norm as E ϭ ȽÎȽ 2 ሻIሻ 2 ϫ 100, [9] where I and Î are the decomposed image before and after thresholding, respectively.…”

Section: Coefficent Thresholdingmentioning

confidence: 99%

“…This research is concerned with the first two blocks. The last two blocks are standard (8)(9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

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Best Parameters Selection for Wavelet Packet-Based Compression of Magnetic Resonance Images

Abu-Rezq

Tolba

Khuwaja

et al. 1999

Computers and Biomedical Research

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“…This is because with the same level of quality, fewer coefficients are needed to represent an image than the number of coefficients required by Discrete Cosine Transform [9]. Although wavelet compression has proved its advantage over Discrete Cosine Transform based compression, wavelet representation of images differ in their choice of wavelets, hence a wavelet can only be the best choice for compression of a certain class of images but not for all kind of images [10,11] .…”

Section: Image Compressionmentioning

confidence: 99%

Multi-level browsing for efficient image transmission

Srinivasan²,

Kulkarni³

Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130)

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Multimedia applications such as distributed digital libraries, digital databases, teleshopping and telebrowsing systems are expected to deliver as minimum data as needed and allow users to quickly browse through images located in remote databases. Currently users often experience delays since a large amount of multimedia data such as images has to be transmitted across networks. Existing network technologies are not advanced enough (many places still use slow links) and the number of users is growing at a rapid pace. This places a further demand on the network bandwidth.Usually different users and applications may require images at different levels of quality but existing browsers only deliver the available data (often pre-compressed data) and do not allow users to specify the quality level of images they need, hence extra data has to be transmitted unnecessarily. The response time can be improved if the requested images are delivered at the requested quality. This paper proposes a framework for multi-level browsers and network graphic user interfaces based on the notion that the users should be allowed to specify the level of quality of the image they need and suitable compression techniques should be employed to minimise needed image data before transmission. Images can be either precompressed at the desired quality levels or further compressed to reduce their volume before they are delivered.

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<title>Comparison of wavelet image coders using picture quality scale (PQS)</title>

Cited by 10 publications

References 22 publications

Oncologic image compression using both wavelet and masking techniques

Oncologic image compression using both wavelet and masking techniques

Best Parameters Selection for Wavelet Packet-Based Compression of Magnetic Resonance Images

Multi-level browsing for efficient image transmission

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