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.

Medical image compression using cubic spline interpolation for low bit-rate telemedicine applications

Abstract: In this paper, a new medical image compression algorithm using cubic spline interpolation (CSI) is presented for telemedicine applications. The CSI is developed in order to subsample image data with minimal distortion and to achieve image compression. It has been shown in literatures that the CSI can be combined with the JPEG or JPEG2000 algorithm to develop a modified JPEG or JPEG2000 codec, which obtains a higher compression ratio and a better quality of reconstructed image than the standard JPEG and JPEG2000 codecs. This paper further makes use of the modified JPEG codec to medical image compression. Experimental results show that the proposed scheme can increase 25~30% compression ratio of original JPEG medical data compression system with similar visual quality. This system can reduce the loading of telecommunication networks and is quite suitable for low bit-rate telemedicine applications.

A chip-set for lossless image compression

Abstract: Two chips have been developed for lossless image compression. The first IC performs a transformation, and the second performs lossless coding. This work presents the transform and coding algorithms and the main architectural features of the chips, and outlines some performance specifications. The image compression/decompression system described reduces storage requirements in high-speed image archival and database applications and speeds the transmission of digital images over communication channels. >

Super-resolution total-variation decoding of JPEG-compressed image data

Abstract: In a digital camera, its output image is sometimes corrupted by additive noise heavily and its noisy image is often compressed with the JPEG encoder. When the coding rate of the JPEG encoder is not high enough, in a JPEG-decoded image there appear noticeable artifacts such as the blocking, the ringing, and the false color artifacts. In the high ISOsensitivity case, even if the coding rate is very high, camera's noise will produce noticeably annoying artifacts in a JPEG-decoded image. This paper presents a restoration-type decoding approach that recovers a quality-improved image from the JPEG-compressed data, while not only suppressing the occurrence of the coding artifacts particular to the JPEG compression but also removing the camera's noise to some extent. This decoding approach is a kind of superresolution image-restoration approach based on the TV (Total Variation) regularization; to reduce the ringing artifacts near sharp edges it selectively restores the DCT coefficients truncated by the JPEG compression, whereas in an originally smooth image region it flattens unnecessary signal variations to eliminate the blocking artifacts and the camera's noise. Extending the standard ROF (Rudin-Osher-Fetami) framework of the TV image restoration, in this paper we construct the super-resolution approach to the JPEG decoding. By introducing the JPEG-compressed data into the fidelity term of the energy functional and adopting a nonlinear cost function constrained by the JPEG-compressed data softly, we define a new energy functional whose minimization gives the super-resolution JPEG decoding.

Median Edge Detector for Lossless Video Coding: An efficient prediction scheme for lossless video coding

Abstract: In this paper, we propose a novel non-linear prediction based method for lossless video coding. This method exploits gradient energies to perform pixel by pixel prediction. A feedback mechanism based on prediction context is also proposed, which prevents the non-linear predictor from over adjusting its predicted value. To exploit the redundancies present in the residues, a context based residue coding scheme is devised. This scheme efficiently predicts sign and magnitude of the residue. Besides computational simplicity of the proposed method, experimental results show lower value of zero order entropy as compared to the competitive algorithms reported in the literature.