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.

Quality improvement technique for JPEG images with fractal image coding

Abstract: This paper proposes a quality improvement technique for JPEG images by using fractal image coding. JPEG coding is a commonly used standard method of compressing images. However, in its decoded images, quantization noise is sometimes visible in high frequency regions, such as the edges of objects. Hence, in order for the JPEG coding to become a more powerful image-coding method, the JPEG image quality must be improved. Therefore, our method solves this problem by adding the obtained codes by the fractal image coding to improve the image quality. Some simulation results verify that the proposed method achieved higher coding-performance than the traditional JPEG coding.

Countering JPEG anti-forensics based on noise level estimation

Abstract: Quantization artifact and blocking artifact are the two types of well-known fingerprints of JPEG compression. Most JPEG forensic techniques are focused on these fingerprints. However, recent research shows that these fingerprints can be intentionally concealed via anti-forensics, which in turn makes current JPEG forensic methods vulnerable. A typical JPEG anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifact to remove the trace of compression history. To deal with this challenge in JPEG forensics, in this paper, we propose a novel countering method based on the noise level estimation to identify the uncompressed images from those forged ones. The experimental results show that the proposed method achieves superior performance on several image databases with only one-dimensional feature. It is also worth emphasizing that the proposed threshold-based method has explicit physical meaning and is simple to be implemented in practice. Moreover, we analyze the strategies available to the investigator and the forger in the case of that they are aware of the existence of each other. Game theory is used to evaluate the ultimate performance when both sides adopt their Nash equilibrium strategies.

Lossless compression of Bayer mask images using an optimal vector prediction technique

Abstract: In this paper a lossless compression technique for Bayer pattern images is presented. The common way to save these images was to colour reconstruct them and then code the full resolution images using one of the lossless or lossy methods. This solution is useful to show the captured images at once, but it is not convenient for efficient source coding. In fact, the resulting full colour image is three times greater than the Bayer pattern image and the compression algorithms are not able to remove the correlations introduced by the reconstruction algorithm. However, the Bayer pattern images present new problems for the coding step. In fact, adjacent pixels belong to different colour bands mixing up different kinds of correlations. In this paper we present a lossless compression procedure based on an optimal vector predictor, where the Bayer pattern is divided into non-overlapped 2×2 blocks, each of them predicted as a vector. We show that this solution is able to exploit the existing correlation giving a good improvement of the compression ratio with respect to other lossless compression techniques, e.g., JPEG-LS.

Direct 3D lossless image compression based on region growing

Abstract: A segmentation-based 3D lossless compression method that exploits the dependencies in all three dimensions of volumetric images is proposed.

Image compression using adaptive variable degree variable segment length chebyshev polynomials

Abstract: In this paper, a new lossy image compression technique based on adaptive variable degree variable segment length Chebyshev polynomials is proposed. The main advantage of this method over JPEG is that it has a direct individual error control where the maximum error in gray level difference between the original and the reconstructed images can be specified by the user. This is a requirement for medical applications where near lossless quality is needed. The compression is achieved by representing the gray level variations across any determined section of a row or column of an image by the coefficients of a Chebyshev polynomial. The performance of the method was evaluated on a number of test images and using some quantitative measures compared to the well known JPEG compression techniques.