𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Coder selection for lossy compression of still images

✍ Scribed by J.A. Garcia; J. Fdez-Valdivia; Rosa Rodriguez-Sánchez; Xose R. Fdez-Vidal

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
949 KB
Volume
35
Category
Article
ISSN
0031-3203

No coin nor oath required. For personal study only.

✦ Synopsis

A criterion for selecting a lossy coder for still images is proposed. The "optimum" coder using the proposed criterion is selected to minimize the maximum Cherno information between the distribution for the original and any distribution from the optimal achievable region. The resulting coder has the property that the best achievable probability of error over the problems of hypothesis testing between these distributions, is greater than the best Bayesian error probability for any other choice of coder. The coder selection procedure may be applied without a knowledge of what distortion measure is more suitable for images or a knowledge of the properties of the human visual perception. Several examples of coder selection are included to illustrate the procedure.

📜 SIMILAR VOLUMES

Segmented Adaptive DPCM for Lossy Compre
Segmented Adaptive DPCM for Lossy Compression of Multispectral MR Images
✍ Jian-Hong Hu; Yao Wang; Patrick Cahill 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 976 KB

of the digitally-based (as opposed to film-based) picture archiving and communications systems (PACS). One of This paper reports a multispectral segmented differential pulse coded modulation (MSDPCM) method for well registered the biggest bottlenecks in this process is the extremely multispectral ma

A minute lossy method for 2D-gel images
A minute lossy method for 2D-gel images compression
✍ Yung-Kuan Chan; Duan-Li Liau; Yung-Fu Chen; Hsien-Chu Wu; Yen-Ping Chu 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 613 KB

## Abstract This article presents a compression method to encode a 2D‐gel image by using hybrid lossless and lossy techniques. In this method, areas containing protein spots are encoded using lossless method while the background is encoded using the lossy method. A 2D‐gel image usually covers a lar

A NEW ALGORITHM FOR LOSSLESS STILL IMAGE
A NEW ALGORITHM FOR LOSSLESS STILL IMAGE COMPRESSION
✍ TREES-JUEN CHUANG; JA-CHEN LIN 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 356 KB

This paper presents a spatial domain method for lossless still image compression using a new scheme: base switching (BS). The given image is partitioned into non-overlapping fixed-size subimages. Different subimages then get different compression ratios according to the base values of the subimages.

An overview of the JPEG 2000 still image
An overview of the JPEG 2000 still image compression standard
✍ Majid Rabbani; Rajan Joshi 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 800 KB

In 1996, the JPEG committee began to investigate possibilities for a new still image compression standard to serve current and future applications. This initiative, which was named JPEG 2000, has resulted in a comprehensive standard (ISO 154447ITU-T Recommendation T.800) that is being issued in six

Assessment of commercial compression alg
Assessment of commercial compression algorithms, of the lossy DCT and lossless types, applied to diagnostic digital image files
✍ Demetrios Okkalides 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 657 KB

The need for diagnostic image compression of the lossy or irreversible type has been declining due to the rapid increase in commercially available formatted hard disk capacity. It is estimated that the latter has increased about three orders of magnitude in the past 14 years while the size of diagno

Forward-adaptive method for context-base
Forward-adaptive method for context-based compression of large binary images
✍ Eugene I. Ageenko; Pasi Fränti 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 154 KB 👁 1 views

A method for compressing large binary images is proposed for applications where spatial access to the image is required. The proposed method is a two-stage combination of forward-adaptive modeling and backwardadaptive context based compression with re-initialization of statistics. The method improve