Topic
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.
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TL;DR: A scheme for improving the performance of JPEG 2000 over noisy channels and it is observed that the worst-case peak signal-to-noise ratio improves significantly compared to the conventional JPEG 2000.
Abstract: A scheme for improving the performance of JPEG 2000 over noisy channels is presented. The detailed operation of the decoder based on a set of rules is provided. It is observed that the worst-case peak signal-to-noise ratio improves significantly compared to the conventional JPEG 2000.
TL;DR: The compression based on content-based non-uniform meshes, spatial transformation for motion compensation of MR images using JPEG which optimizes the above said issue and also aids in telemedicine applications is explained.
Abstract: The MR images play a major role in the diagnosis of vital organs of the human body. Huge amount of medical image data is generated on a daily basis. This data needs to be stored for future study and follow up. This requires a large amount of storage space which is especially true for three dimensional (3-D) medical data formed by image sequences. This has resulted in image compression being an important issue in reducing the cost of data storage and transmission time. Mesh based compression is one among them. JPEG is the currently accepted industry standard for still image compression. This paper explains the compression based on content-based non-uniform meshes, spatial transformation for motion compensation of MR images using JPEG which optimizes the above said issue and also aids in telemedicine applications. Compression rate achieved in 3 D wavelet scheme is 1.86(8), Multi dimensional Layered Zero Coding is 2.16(8) and in our coding using JPEG it is found to be 2.64.
Proceedings Article•
01 Sep 2002TL;DR: The relationship between lossless and lossy coding based on JPEG2000 is discussed and an efficient lossless coding method for lossy images is proposed, which is useful for the dubbing and editing of images and video sequences.
Abstract: We consider lossy image coding methods using integer wavelet transforms and describe new applications of lossless JPEG2000 coding for re-encoding without the use of any coding parameters, such as the target rate. The relationship between lossless and lossy coding based on JPEG2000 is discussed and an efficient lossless coding method for lossy images is proposed. The proposed method is useful for the dubbing and editing of images and video sequences. By providing the result of some simulations, we demonstrate the effectiveness of the proposed method.
TL;DR: This work introduces a new idea called the soft decision quantization and integrate it with the binary arithmetic QM coder and provides a better compression ratio than purely lossless compression schemes and has a better reconstructed image quality than lossy ones.
Abstract: In this work, we investigate the nearly lossless image compression technique, which provides abetter compression ratio than purely lossless compression schemes and has a better reconstructed image quality than lossy ones. In particular, we introduce a new idea called the soft decisionquantization and integrate it with the binary arithmetic QM coder. The superior performance of the developed algorithm is demonstrated with numerical experiments. Keywords: nearly lossless, semi-lossless, image compression, soft decision quantization, QM coder. 1 INTRODUCTION Image compression methods can be categorized into two classes: lossless and lossy schemes.Entropy encoding is an example of lossless compression which removes redundancy among sym- bols without actual loss of information so that the image can be reconstructed exactly as theoriginal one. Quantization is applied in lossy compression schemes to achieve a better coding gain at the expense of information loss and, consequently, the reconstructed image cannot be thesame as the original one. In applications such as picture archiving and medical/legal image trans-mission, high compression efficiency and image fidelity are both required. We have to consider acareful balance between the desired image quality and the cost of storage and transmission. Theresulting scheme is called high fidelity image compression, which reduces the distortion of tra-ditional lossy compression schemes while providing a better rate deduction than purely losslessschemes. In this work, we propose a new approach to achieve high fidelity image compression.The basic idea is to divide the entire quantization range into 3 intervals: LOW, HIGH and Don'tCARE when performing quantization. The LOW interval is always encoded with 0, the HIGH
TL;DR: In this paper , a lightweight image compression method combining JPEG and number theoretic transformation (NTT) is proposed to address the problem of low compression ratio and increase the requirement for transmission rate.
Abstract: Joint photographic experts group (JPEG) is the common compression technology of image transmission with the Internet of Things (IoT), which probably causes low compression ratio (CR) and increases the requirement for transmission rate. Therefore, a lightweight image compression method combining JPEG and number theoretic transformation (NTT) is proposed to address the above problems. Simulation results show that the proposed method has higher CR than JPEG, embedded zerotree wavelet (EZW), and combining singular value decomposition with wavelet difference reduction (SVD‐WDR) compression algorithms, which reduces the requirement of IoT devices for transmission rate.