Evaluation of Region-of-Interest coders using perceptual image quality assessments

Garcia-Alvarez, J. C.; Führ, Hartmut; Castellanos-Domínguez, G.

doi:10.1016/j.jvcir.2013.09.003

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…There are several lossless image compression algorithms available in the literature [3][4][5][6]. The images are split into region of interest (ROI) and non-region of interest (NROI) for selective compression [7][8][9][10]. Based on spatial prediction of these regions the error values are transformed and encoded in lossy and lossless mode using wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Prediction and Quality Measurements for Medical Image Compression

Sophia

Anitha

2016

Advances in Intelligent Systems and Computing

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This paper presents a prediction-based compression algorithm for medical image containing region of interest. The medical image on the whole consumes a lot of memory space which makes them difficult for storage and transmission. In a medical image with only a particular part needed for diagnosis, the important decision that has to be made is whether to go for block compression or region-based compression. Here region-based compression plays a vital role since a particular region alone can be preserved and the other regions can be compressed in a lossy way. Such methods are of great interest in tele-radiology applications with large storage requirements. Here, the quality of compression can be measured by capturing the size of the selected regions. A new method for calculating total compression ratio and total bits per pixel is proposed for such selective image compression algorithms. Since selected area of medical images is compressed lossless, the performance of the proposed system is compared with other lossless compression algorithms. The results showed comparatively good performance.

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Section: Introductionmentioning

confidence: 99%

Region-Based Prediction and Quality Measurements for Medical Image Compression

Sophia

Anitha

2016

Advances in Intelligent Systems and Computing

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“…These images may lose information due to noises and manipulations by hackers during communication, which is not tolerable in the case of medical images [6]. The affected data may become an obstacle in diagnostic analysis at the destination.…”

Section: Introductionmentioning

confidence: 99%

Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities

et al. 2015

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Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

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Representation of image content based on RoI-BoW

Zhang

Zhao

et al. 2015

Journal of Visual Communication and Image Representation

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Evaluation of Region-of-Interest coders using perceptual image quality assessments

Cited by 6 publications

References 35 publications

Region-Based Prediction and Quality Measurements for Medical Image Compression

Region-Based Prediction and Quality Measurements for Medical Image Compression

Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities

Representation of image content based on RoI-BoW

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