An overview of JPEG-2000

By Michael W. Marcellin, Michael J. Gormish, A. Bilgin, M. Boliek. Proceedings DCC 2000. Data Compression Conference, 2000

520 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/19b7acd9b98e064e0704e2cc5760e1199fb4f785

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Automatic foveation for video compression using a neurobiological model of visual attention (2004, background): “This type of approach has been particularly successful, and those properties which are well defined (e.g., contrast sensitivity function, importance of motion, and temporal masking effects) are already implemented in modern video and still-image codecs [20], [21].”
JPEG2000: standard for interactive imaging (2002, background): “1) Lossy Compression: Fig. 21 [ 3 ] provides a performance comparison for two different JPEG code-streams and three different JPEG2000 code-streams representing the “bike” image (gray-scale, 2048 2560) from the JPEG2000 test set.”
Learning Sparsifying Transforms (2013, background): “The zeros of essentially define the subspace to which the signal belongs, and the total number of these zeros is called co-sparsity [12], [15].”
An overview of the JPEG 2000 still image compression standard (2002, background): “Although the JPEG-LS has only a small performance advantage (3.4%) over JPEG2000 for the images considered in this study, it has been shown that for certain classes of imagery (e.g., the ‘‘cmpnd1’’ compound document from the JPEG2000 test set), the JPEG-LS bit-rate is only 60% of that of JPEG2000 [ ”
Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization (2009, methodology): “We start by reviewing prior related work, following by the presentation of our proposed algorithm and the first results showing the computational and reconstruction advantages of this new approach.”
Sparse Representation for Color Image Restoration (2008, background): “Indeed, these various transforms have led to highly effective algorithms in many applications in image processing, such as compression [14], de-noising [15]–[18], inpainting [19], and more.”
Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients (2001, background): “exploits intrascale dependencies alone [ 19 , 20].”
Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “, in Discrete Cosine Transform (DCT) or wavelet domains) plays the central role in most lossy compression standards like JPEG [71], JPEG-2000 [72] and”