scispace - formally typeset
Search or ask a question
Topic

Lossless compression

About: Lossless compression is a research topic. Over the lifetime, 13218 publications have been published within this topic receiving 199941 citations.


Papers
More filters
Book
01 Mar 1993
TL;DR: Fractal Image Compression (FI) as discussed by the authorsractals are geometric or data structures which do not simplify under magnification and can be described in terms of a few succinct rules, while the fractal contains much or all the image information.
Abstract: Fractals are geometric or data structures which do not simplify under magnification. Fractal Image Compression is a technique which associates a fractal to an image. On the one hand, the fractal can be described in terms of a few succinct rules, while on the other, the fractal contains much or all of the image information. Since the rules are described with less bits of data than the image, compression results. Data compression with fractals is an approach to reach high compression ratios for large data streams related to images. The high compression ratios are attained at a cost of large amounts of computation. Both lossless and lossy modes are supported by the technique. The technique is stable in that small errors in codes lead to small errors in image data. Applications to the NASA mission are discussed.

673 citations

Journal Article
TL;DR: This article describes a simple general-purpose data compression algorithm, called Byte Pair Encoding (BPE), which provides almost as much compression as the popular Lempel, Ziv, and Welch method.
Abstract: Data compression is becoming increasingly important as a way to stretch disk space and speed up data transfers. This article describes a simple general-purpose data compression algorithm, called Byte Pair Encoding (BPE), which provides almost as much compression as the popular Lempel, Ziv, and Welch (LZW) method [3, 2]. (I mention the LZW method in particular because it delivers good overall performance and is widely used.) BPE’s compression speed is somewhat slower than LZW’s, but BPE’s expansion is faster. The main advantage of BPE is the small, fast expansion routine, ideal for applications with limited memory. The accompanying C code provides an efficient implementation of the algorithm.

657 citations

Journal ArticleDOI
TL;DR: The biomimetic CMOS dynamic vision and image sensor described in this paper is based on a QVGA array of fully autonomous pixels containing event-based change detection and pulse-width-modulation imaging circuitry, which ideally results in lossless video compression through complete temporal redundancy suppression at the pixel level.
Abstract: The biomimetic CMOS dynamic vision and image sensor described in this paper is based on a QVGA (304×240) array of fully autonomous pixels containing event-based change detection and pulse-width-modulation (PWM) imaging circuitry. Exposure measurements are initiated and carried out locally by the individual pixel that has detected a change of brightness in its field-of-view. Pixels do not rely on external timing signals and independently and asynchronously request access to an (asynchronous arbitrated) output channel when they have new grayscale values to communicate. Pixels that are not stimulated visually do not produce output. The visual information acquired from the scene, temporal contrast and grayscale data, are communicated in the form of asynchronous address-events (AER), with the grayscale values being encoded in inter-event intervals. The pixel-autonomous and massively parallel operation ideally results in lossless video compression through complete temporal redundancy suppression at the pixel level. Compression factors depend on scene activity and peak at ~1000 for static scenes. Due to the time-based encoding of the illumination information, very high dynamic range - intra-scene DR of 143 dB static and 125 dB at 30 fps equivalent temporal resolution - is achieved. A novel time-domain correlated double sampling (TCDS) method yields array FPN of 56 dB (9.3 bit) for >10 Lx illuminance.

632 citations

Proceedings ArticleDOI
01 Jul 2000
TL;DR: A new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning, coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction sees improvements in error by a factor four compared to other progressive coding schemes.
Abstract: We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes.

630 citations

Proceedings ArticleDOI
31 Mar 1996
TL;DR: LOCO-I as discussed by the authors combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies.
Abstract: LOCO-I (low complexity lossless compression for images) is a novel lossless compression algorithm for continuous-tone images which combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.

625 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
90% related
Image processing
229.9K papers, 3.5M citations
89% related
Convolutional neural network
74.7K papers, 2M citations
87% related
Deep learning
79.8K papers, 2.1M citations
86% related
Artificial neural network
207K papers, 4.5M citations
85% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023299
2022673
2021372
2020435
2019511
2018500