4,183 research outputs found
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
On color image quality assessment using natural image statistics
Color distortion can introduce a significant damage in visual quality
perception, however, most of existing reduced-reference quality measures are
designed for grayscale images. In this paper, we consider a basic extension of
well-known image-statistics based quality assessment measures to color images.
In order to evaluate the impact of color information on the measures
efficiency, two color spaces are investigated: RGB and CIELAB. Results of an
extensive evaluation using TID 2013 benchmark demonstrates that significant
improvement can be achieved for a great number of distortion type when the
CIELAB color representation is used
JPEG2000 Image Compression on Solar EUV Images
For future solar missions as well as ground-based telescopes, efficient ways
to return and process data have become increasingly important. Solar Orbiter,
e.g., which is the next ESA/NASA mission to explore the Sun and the
heliosphere, is a deep-space mission, which implies a limited telemetry rate
that makes efficient onboard data compression a necessity to achieve the
mission science goals. Missions like the Solar Dynamics Observatory (SDO) and
future ground-based telescopes such as the Daniel K. Inouye Solar Telescope, on
the other hand, face the challenge of making petabyte-sized solar data archives
accessible to the solar community. New image compression standards address
these challenges by implementing efficient and flexible compression algorithms
that can be tailored to user requirements. We analyse solar images from the
Atmospheric Imaging Assembly (AIA) instrument onboard SDO to study the effect
of lossy JPEG2000 (from the Joint Photographic Experts Group 2000) image
compression at different bit rates. To assess the quality of compressed images,
we use the mean structural similarity (MSSIM) index as well as the widely used
peak signal-to-noise ratio (PSNR) as metrics and compare the two in the context
of solar EUV images. In addition, we perform tests to validate the scientific
use of the lossily compressed images by analysing examples of an on-disk and
off-limb coronal-loop oscillation time-series observed by AIA/SDO.Comment: 25 pages, published in Solar Physic
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
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