JPEG column: 82nd JPEG meeting in Lisbon, Portugal

2019 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Antonio Pinheiro
Keyword(s):  
Image Coding ◽  
Digital Images ◽  
Image Representation ◽  
Jpeg 2000 ◽  
Compression Efficiency ◽  
Common Representation ◽  
Limited Success

JPEG has been the most common representation format of digital images for more than 25 years. Other image representation formats have been standardised by JPEG committee like JPEG 2000 or more recently JPEG XS. Furthermore, JPEG has been extended with new functionalities like HDR or alpha plane coding with the JPEG XT standard, and more recently with a reference software. Another solutions have been also proposed by different players with limited success. The JPEG committee decided it is the time to create a new working item, named JPEG XL, that aims to develop an image coding standard with increased quality and flexibility combined with a better compression efficiency. The evaluation of the call for proposals responses had already confirmed the industry interest, and development of core experiments has now begun. Several functionalities will be considered, like support for lossless transcoding of images represented with JPEG standard.

Interplay between JPEG-2000 image coding and quality estimation

2013 ◽  
Author(s):  
Guilherme O. Pinto ◽  
Sheila S. Hemami
Keyword(s):  
Image Coding ◽  
Jpeg 2000 ◽  
Quality Estimation

JPEG 2000 still image coding versus other standards

2000 ◽  
Author(s):  
Diego Santa-Cruz ◽  
Touradj Ebrahimi ◽  
Joel Askelof ◽  
Mathias Larsson ◽  
Charilaos A. Christopoulos
Keyword(s):  
Image Coding ◽  
Jpeg 2000 ◽  
Still Image

JPEG 2000 Image Coding Standard

2008 ◽  
pp. 379-386
Author(s):  
Hong Man ◽  
Alen Docef ◽  
Faouzi Kossentini
Keyword(s):  
Image Coding ◽  
Jpeg 2000

Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

2020 ◽  
pp. 2150021
Author(s):  
Urvashi Sharma ◽  
Meenakshi Sood ◽  
Emjee Puthooran
Keyword(s):  
Medical Image ◽  
Region Of Interest ◽  
Block Size ◽  
Threshold Value ◽  
Hospital Performance ◽  
Jpeg 2000 ◽  
Compression Method ◽  
Compression Efficiency ◽  
Important Region

A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.

Image coding design considerations for cascaded encoding-decoding cycles and image editing: analysis of JPEG 1, JPEG 2000, and JPEG XR / HD Photo

2008 ◽  
Author(s):  
Gary J. Sullivan ◽  
Shijun Sun ◽  
Shankar Regunathan ◽  
Daniel Schonberg ◽  
Chengjie Tu ◽  
...  
Keyword(s):  
Image Coding ◽  
Image Editing ◽  
Jpeg 2000 ◽  
Design Considerations ◽  
Jpeg Xr

Study of the Discrete Wavelet Transform Based on JPEG2000 Image Compression Algorithm

2013 ◽  
Vol 464 ◽  
pp. 411-415
Author(s):  
Jin Cai ◽  
Shuo Wang
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Image Coding ◽  
Dynamic Range ◽  
Compression Algorithm ◽  
Discrete Wavelet ◽  
Coding System ◽  
Jpeg 2000 ◽  
Image Compression Algorithm

JPEG 2000 is a new image coding system that uses state-of-the-art compression techniques based on wavelet technology. As interactive multimedia technologies evolve, the requirements for the file format used to store the image data continue to evolve. The size and bit depth collected for an image to increase the resolution and extend the dynamic range and color gamut. Discrete Wavelet transform based embedded image coding method is the basis of JPEG2000. Image compression algorithm for the proper use and display of the image is a requirement for digital photography.

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