Lossless JPEG

About: Lossless JPEG is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 51110 citations. The topic is also known as: Lossless JPEG & .jls.

VLSI chip solution for lossless medical imagery compression

Abstract: This paper describes two VLSI implementations that provide an effective solution to compressing medical image data in real time. The implementations employ a lossless data compression algorithm, known as the Rice algorithm. The first chip set was fabricated in 1991. The encoder can compress at 20 Msamples/sec and the decoder decompresses at the rate of 10 Msamples/sec. The chip set is available commercially. The second VLSI chip development is a recently fabricated encoder that provides improvements for coding low entropy data and incorporates features that simplify system integration. A new decoder is scheduled to be designed and fabricated in 1994. The performance of the compression chips on a suite of medical images has been simulated. The image suite includes CT, MR, angiographic images, and nuclear images. In general, the single-pass Rice algorithm compression performance exceeds that of two-pass, lossless, Huffman-based JPEG. The overall compression performance of the Rice algorithm implementations exceeds that of all algorithms tested including arithmetic coding, UNIX compress, UNIX pack, and gzip.

Image tampering detection using Bayer interpolation and JPEG compression

Abstract: In this paper, we describe a technique to detect image tampering using two different methods. The first is based on the Bayer interpolation process and its consequences in the Fourier domain. The second uses artifacts of the JPEG compression and more particularly in the JPEG frame observable in the Fourier domain.

Diagnostically lossless compression of medical images-2

Abstract: Medical images are very important for diagnostics and therapy However, digital imaging generates large amounts of data which need to be compressed, without loss of relevant information, to economize storage space and allow speedy transfer In this research three techniques are implemented for medical image compression, which provide high compression ratios with no loss of diagnostic quality Different image modalities are employed for experiments in which X-rays, MRI, CT scans, Ultrasounds and Angiograms are included The proposed schemes are evaluated by comparing with existing standard compression techniques like JPEG, lossless JPEG2000, LOCOI and Huffman Coding In a medical image only a small region is diagnostically relevant while the remaining image is much less important This is called Region of Interest (ROI) The first approach compresses the ROI strictly losslessly and the remaining regions of the image with some loss In the second approach an image is first compressed at a high compression ratio but with loss, and the difference image is then compressed losslessly Difference image contain less data and is compressed more compactly than original Third approach exploits inter-image redundancy for similar modality and same part of human body More similarity means less entropy which leads to higher compression performance The overall compression ratio is combination of lossy and lossless compression ratios The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio These techniques are based on self designed Neural Network Vector Quantizer (NNVQ) and Huffman coding Their clever combination is used to get lossless effect These are spatial domain techniques and do not require frequency domain transformation An overall compression ratio of 6-14 is obtained for images with proposed methods Whereas, by compressing same images by a lossless JPEG2K and Huffman, compression ratio of 2 is obtained at most The main contribution of the research is higher compression ratios than standard techniques in lossless scenario This result will be of great importance for data management in a hospital and for teleradiology

New watermarking algorithm with data authentication and reduction for JPEG image

Abstract: This paper proposes a novel fragile watermarking algorithm, designated as the compression-watermarking algorithm (CWA), which inserts watermark information in a JPEG image by modifying the last nonzero coefficient in each discrete cosine transform (DCT) quantized block. The proposed algorithm not only provides an authentication capability, but also decreases the size of JPEG compressed-domain images. The experimental results show that CWA has virtually no impact on the visual quality of the watermarked images and is highly sensitive to image modifications. Furthermore, it is found that CWA reduces the size of watermarked image by as much as 6.3% when applied to the watermarking of standard JPEG test images. Therefore, CWA provides a feasible solution for image authentication and data reduction in DCT-based domains such as JPEG and MPEG-family coders/decoders.

Lossless image coding using wavelets and variable block size segmentation

Abstract: We propose a lossless image compression scheme using wavelet decomposition. Wavelet decomposition of an image f(x,y) at a resolution 2/sup j/ consists of an approximated image at a resolution 2/sup j-1/ and three detail images along the horizontal, vertical and diagonal directions. The approximated wavelet coefficients are encoded using a variable block size segmentation (VBSS) algorithm proposed by Ranganathan et.al. (see IEEE Trans. on Image Proc., vol.4, no.10,p.1396-1406, 1995) and the detail signals are encoded using directional prediction and categorization similar to that in the VBSSS algorithm. The residual error due to the finite precision arithmetic is encoded using adaptive arithmetic coding (AAC). The performance of the proposed approach is comparable to that exhibited by JPEG lossless schemes while being better than the Huffman, the Lempe-Ziv and arithmetic coding.