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.

JPEG and motion-JPEG (M-JPEG) compression

Abstract: This chapter describes Joint Photographic Experts Group (JPEG), which is a standard for lossy compression of still images based upon the discrete cosine transform (DCT). JPEG is rarely used directly in video, but it forms the basis of Motion-JPEG (M-JPEG) used in desktop video editing and digital video (DV) compression. Also, JPEG techniques form the core of MPEG refers to the use of a JPEG-like algorithm to compress each field or frame in a sequence of video fields or frames. M-JPEG systems use the methods of JPEG, if conform to the ISO/IEC JPEG standard. DV is a specific type of M-JPEG that is well standardized. The JPEG standard, cited in the margin, defines four modes: sequential, hierarchical, progressive, and lossless. The JPEG standard accommodates discrete cosine transform (DCT) coefficients having from 2 to 16 bits, and accommodates two different entropy coders (Huffman and arithmetic). The ISO/IEC standard for JPEG defines a bitstream, consistent with the original expectation that JPEG would be used across communication links.

JPEG Compressed Domain Face Recognition:Different Stages and Different Features

Abstract: JPEG compression standard is widely used for reducing the volume of images that are stored or transmitted via networks. In biometrics datasets, face images are usually stored in JPEG compressed format, and should be fully decompressed to be used in a face recognition system. Recently, in order to reduce the time and complexity of decompression step, face recognition in compressed domain is considered as an emerging topic in face recognition systems. In this paper, we have tested different feature spaces, including PCA and ICA in various stages of JPEG compressed domain. The goal of these tests was to determine the best stage in JPEG compressed domain and the best features to be used in face recognition process, regarding the trade-off between the decompression overhead reduction and recognition accuracy.The experiments were conducted on FERET and FEI face databases, and results have been compared in various stages of JPEG compressed domain. The results show the superiority of zigzag scanned stagecompared to other stages and ICAfeature space compared to other feature spaces,both in terms of recognition accuracy and computational complexity.

Steganography based on baseline sequential JPEG compression

Abstract: Information hiding in Joint Photographic Experts Group (JPEG) compressed images are investigated in this paper. Quantization is the source of information loss in JPEG compression process. Therefore, information hidden in images is probably destroyed by JPEG compression. This paper presents an algorithm to reliably embed information into the JPEG bit streams in the process of JPEG encoding. Information extraction is performed in the process of JPEG decoding. The basic idea of our algorithm is to modify the quantized direct current (DC) coefficients and non-zero alternating current (AC) coefficients to represent one bit information (0 or 1). Experimental results on gray images using baseline sequential JPEG encoding show that the cover images (images without secret information) and the stego-images (images with secret information) are perceptually indiscernible.

Meaningful secret image sharing for JPEG images with arbitrary quality factors.

Abstract: JPEG is the most common format for storing and transmitting photographic images on social network platforms. JPEG image is widely used in people's life because of their low storage space and high visual quality. Secret image sharing (SIS) technology is important to protect image data. Traditional SIS schemes generally focus on spatial images, however there is little research on frequency domain images. In addition, the current tiny research on SIS for JPEG images only focuses on JPEG images with a compression quality factor (QF) of 100. To overcome the limitation of JPEG images in SIS, we propose a meaningful SIS for JPEG images to operate the quantized DCT coefficients of JPEG images. The random elements utilization model is applied to achieve meaningful shadow images. Our proposed scheme has a better quality of the shadow images and the recovered secret image. Experiment results and comparisons indicate the effectiveness of the scheme. The scheme can be used for JPEG images with any compression QF. Besides, the scheme has good characteristics, such as (k,n) threshold, extended shadow images.