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.

Research of color image lossless compression algorithm based on DWT-YCOCG-SPIHT-CABAC

Abstract: In this paper, an optimized algorithm which combines improved SPIHT algorithm and segmented CABAC algorithm is presented to realize color images lossless compression. At the same time, wavelet transform is used to remove spatial redundancy, and YCoCg spectrum transformation is used to effectively remove spectrum redundancy. Experiment results show that the algorithm in this paper can effectively improve image lossless compression ratio, which is 32.62% higher than ZIP lossless compression ratio on average.

Multi-Regional Adaptive Image Compression (AIC) for Hip Fractures in Pelvis Radiography

Abstract: High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content.

Based on discrete orthogonal tchebichef transform for image compression

Abstract: After analyzing joint photographic experts group(JPEG)compression based on the discrete cosine transform(DCT), discussing orthogonal basis functions as the kernel for DCT and discrete tchebichef transform(DTT),a novel approach of based on DTT for image compression is proposed.According to JPEG standard quantization table,using information entropy,quantization table of algorithm of based on DTT for image compression is designed.A comparison for color image between based on DTT and JPEG image compression and reconstruction using MATLAB R2010B.An simulation example is presented two methods have almost the same performance in peak signal to noise ratio(PSNR),but image compression based on DTT perform more compact than JPEG in encode length.

Lossless, Multiband, on Board, Compression of Hyperspectral Images

Abstract: Hyperspectral remote sensing produces a huge amount of three-dimensional digital data: the hyperspectral images. Hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. They are considered a useful tool in different real-life applications. In this paper we propose a novel approach for the efficient lossless compression of hyperspectral images, which is based on a predictive coding model. Our approach relies on a three-dimensional predictive structure that uses, one or more, previous bands as references to exploit the redundancies among the third dimension. The proposed technique uses limited resources in terms of CPU and memory usage. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.