254 research outputs found
Exclusive-or preprocessing and dictionary coding of continuous-tone images.
The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression
Language Modeling Is Compression
It has long been established that predictive models can be transformed into
lossless compressors and vice versa. Incidentally, in recent years, the machine
learning community has focused on training increasingly large and powerful
self-supervised (language) models. Since these large language models exhibit
impressive predictive capabilities, they are well-positioned to be strong
compressors. In this work, we advocate for viewing the prediction problem
through the lens of compression and evaluate the compression capabilities of
large (foundation) models. We show that large language models are powerful
general-purpose predictors and that the compression viewpoint provides novel
insights into scaling laws, tokenization, and in-context learning. For example,
Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to
43.4% and LibriSpeech samples to 16.4% of their raw size, beating
domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
Finally, we show that the prediction-compression equivalence allows us to use
any compressor (like gzip) to build a conditional generative model
On the Effectiveness of Video Recolouring as an Uplink-model Video Coding Technique
For decades, conventional video compression formats have advanced via incremental improvements with
each subsequent standard achieving better rate-distortion (RD) efficiency at the cost of increased encoder
complexity compared to its predecessors. Design efforts have been driven by common multi-media use cases
such as video-on-demand, teleconferencing, and video streaming, where the most important requirements are
low bandwidth and low video playback latency. Meeting these requirements involves the use of computa-
tionally expensive block-matching algorithms which produce excellent compression rates and quick decoding
times.
However, emerging use cases such as Wireless Video Sensor Networks, remote surveillance, and mobile
video present new technical challenges in video compression. In these scenarios, the video capture and
encoding devices are often power-constrained and have limited computational resources available, while the
decoder devices have abundant resources and access to a dedicated power source. To address these use cases,
codecs must be power-aware and offer a reasonable trade-off between video quality, bitrate, and encoder
complexity. Balancing these constraints requires a complete rethinking of video compression technology.
The uplink video-coding model represents a new paradigm to address these low-power use cases, providing
the ability to redistribute computational complexity by offloading the motion estimation and compensation
steps from encoder to decoder. Distributed Video Coding (DVC) follows this uplink model of video codec
design, and maintains high quality video reconstruction through innovative channel coding techniques. The
field of DVC is still early in its development, with many open problems waiting to be solved, and no defined
video compression or distribution standards. Due to the experimental nature of the field, most DVC codec
to date have focused on encoding and decoding the Luma plane only, which produce grayscale reconstructed
videos.
In this thesis, a technique called “video recolouring” is examined as an alternative to DVC. Video recolour-
ing exploits the temporal redundancies between colour planes, reducing video bitrate by removing Chroma
information from specific frames and then recolouring them at the decoder.
A novel video recolouring algorithm called Motion-Compensated Recolouring (MCR) is proposed, which
uses block motion estimation and bi-directional weighted motion-compensation to reconstruct Chroma planes
at the decoder. MCR is used to enhance a conventional base-layer codec, and shown to reduce bitrate by
up to 16% with only a slight decrease in objective quality. MCR also outperforms other video recolouring
algorithms in terms of objective video quality, demonstrating up to 2 dB PSNR improvement in some cases
Discrete Wavelet Transforms
The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications
The 1995 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
1994 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
Image Processing Using FPGAs
This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs
On the design of fast and efficient wavelet image coders with reduced memory usage
Image compression is of great importance in multimedia systems and
applications because it drastically reduces bandwidth requirements for
transmission and memory requirements for storage. Although earlier
standards for image compression were based on the Discrete Cosine
Transform (DCT), a recently developed mathematical technique, called
Discrete Wavelet Transform (DWT), has been found to be more efficient
for image coding.
Despite improvements in compression efficiency, wavelet image coders
significantly increase memory usage and complexity when compared with
DCT-based coders. A major reason for the high memory requirements is
that the usual algorithm to compute the wavelet transform requires the
entire image to be in memory. Although some proposals reduce the memory
usage, they present problems that hinder their implementation. In
addition, some wavelet image coders, like SPIHT (which has become a
benchmark for wavelet coding), always need to hold the entire image in
memory.
Regarding the complexity of the coders, SPIHT can be considered quite
complex because it performs bit-plane coding with multiple image scans.
The wavelet-based JPEG 2000 standard is still more complex because it
improves coding efficiency through time-consuming methods, such as an
iterative optimization algorithm based on the Lagrange multiplier
method, and high-order context modeling.
In this thesis, we aim to reduce memory usage and complexity in
wavelet-based image coding, while preserving compression efficiency. To
this end, a run-length encoder and a tree-based wavelet encoder are
proposed. In addition, a new algorithm to efficiently compute the
wavelet transform is presented. This algorithm achieves low memory
consumption using line-by-line processing, and it employs recursion to
automatically place the order in which the wavelet transform is
computed, solving some synchronization problems that have not been
tackled by previous proposals. The proposed encodeOliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826Palanci
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