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.

Implementation of DCT and IDCT based image compression and decompression on FPGA

Abstract: In the last two decades the advancement in data communication techniques was significant, during the explosive growth of the Internet and demand for using multimedia has increased. Video and audio data streams require a bandwidth to be transferred in an uncompressed form. Several ways of compressing multimedia streams evolved. Image and video compression is one of the major components used in video — telephony, videoconferencing and multimedia — related applications. The discrete cosine transform and its inverse can be used for transform coding. This paper analysis the different dct approaches and it also describes the design and implementation of a 1-Dimensional 8 words DCT core that can be used in most of video/ audio compression, such as JPEG. The Discrete Cosine Transform has long been the basic transform coding method for the JPEG and MPEG standards. It helps separate the image into parts (or spectral sub-bands) of differing importance — with respect to the spatial quality of the image. The core was designed taking into consideration maximum area optimization. It can process audio frames and JPEG still images where high speed is an important issue. It accepts 8-bit words as an input and consume about 512-clock cycle to process eight 8-bit words.

Design of jpeg image compression scheme with a particle swarm optimization-based quantization table

Abstract: Usage of Image has been increasing and used in many applications. Some applications such as the transmission of images in computer and mobile environments cannot use images directly due to the large amount of memory space needed to store these images. Image compression has a very important role in digital image processing and for effective transmission and storing of digital images. There are various techniques that can be used in image compression. Today JPEG algorithm has become the de facto standard in image compression. The source of its excellent compression ability is the quantization table which determine which frequency components of the Discrete Cosine Transform (DCT) will be neglected. The JPEG default quantization table is generated from a series psycho-visual experiments from several angle points of experimental views. Particle Swarm Optimization (PSO) is a biologically-inspired optimization algorithm and has been experimentally demonstrated to perform excellent to solve many optimization problems by finding out the global best solution in a complicated search space. In this paper, to enhance the accuracy of the JPEG image compression algorithm, the PSO algorithm has been used to search the optimum quantization table. Simulation results show that the performance of the standard JPEG method can be improved by the proposed method in terms of PSNR and MSE. The proposed color image compression method has produced an average PSNR gain of 69.874 % compared with the standard JPEG color image compression method.

High Capacity Steganographic Scheme for JPEG Compression Using Particle Swarm Optimization

Abstract: A steganographic scheme for JPEG compressed image with high capacity and with good quality of the stego-image was presented. A quantization table of size 16*16 was used instead of the commonly used size 8*8 in most JPEG compression to obtain higher embedding capacity. In addition, to improve the quality of the stego-image, particle swarm optimization (PSO) was applied to find an optimal substitution matrix to transform the secret data into the best fit for the cover image before embedding. The experimental results show that, for the proposed scheme, the improvement of the quality of the stego-image and a higher capacity of the secret data was achieved.

Lossless image compression using wavelet decomposition

Abstract: From a multiresolution perspective, a 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. In the first scheme, the approximated wavelet coefficients are encoded using variable block size segmentation (VBSS) algorithm and the detail signals are encoded using directional prediction and categorization. The residual error due to the finite precision arithmetic is significant and is encoded using adaptive arithmetic encoding technique. In the alternate scheme, we propose a new concept of multiresolution which avoids the finite precision arithmetic errors. The approximated image in the alternate scheme is a decimated version of the original image. The equivalence of the alternate multiresolution scheme to the original multiresolution scheme is also analyzed mathematically. The performance of scheme one is comparable to that exhibited by JPEG lossless schemes.

Lossless and Near-Lossless Compression of Hyperspectral Images Based on Search for L_∞ Minimum and Coset Coding

Abstract: Distributed source coding(DSC) is applied to hyperspectral image compression due to its low complexity and error resilienceIn the framework of typical scalar coset coding based distributed compression method(s-DSC),it is pointed out in this paper that the infinity-norm minimization should be the best criterion for prediction,and a sub-optimal prediction method based on search for L∞ minimum is proposed to approach the criterionIn addition,the compression scheme is extended to near-lossless compressionThe experimental results show that the lossless compression bitrate of the proposed method is reduced by about 025bpp compared to s-DSC and the near-lossless compression outperforms JPEG-LS significantlyOwing to the advantages of low complexity,high performance and error resilience,the proposed method is quite suitable for onboard compression