448 research outputs found
Topographic map visualization from adaptively compressed textures
Raster-based topographic maps are commonly used in geoinformation systems to overlay geographic entities on top of digital terrain models. Using compressed texture formats for encoding topographic maps allows reducing latency times while visualizing large geographic datasets. Topographic maps encompass high-frequency content with large uniform regions, making current compressed texture formats inappropriate for encoding them. In this paper we present a method for locally-adaptive compression of topographic maps. Key elements include a Hilbert scan to maximize spatial coherence, efficient encoding of homogeneous image regions through arbitrarily-sized texel runs, a cumulative run-length encoding supporting fast random-access, and a compression algorithm supporting lossless and lossy compression. Our scheme can be easily implemented on current programmable graphics
hardware allowing real-time GPU decompression and rendering of bilinear-filtered topographic maps.Postprint (published version
Learning to compress and search visual data in large-scale systems
The problem of high-dimensional and large-scale representation of visual data
is addressed from an unsupervised learning perspective. The emphasis is put on
discrete representations, where the description length can be measured in bits
and hence the model capacity can be controlled. The algorithmic infrastructure
is developed based on the synthesis and analysis prior models whose
rate-distortion properties, as well as capacity vs. sample complexity
trade-offs are carefully optimized. These models are then extended to
multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is
further evolved as a powerful deep neural network architecture with fast and
sample-efficient training and discrete representations. For the developed
algorithms, three important applications are developed. First, the problem of
large-scale similarity search in retrieval systems is addressed, where a
double-stage solution is proposed leading to faster query times and shorter
database storage. Second, the problem of learned image compression is targeted,
where the proposed models can capture more redundancies from the training
images than the conventional compression codecs. Finally, the proposed
algorithms are used to solve ill-posed inverse problems. In particular, the
problems of image denoising and compressive sensing are addressed with
promising results.Comment: PhD thesis dissertatio
Neural Space-filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to
infer a context-based scan order for a set of images. Linear ordering of pixels
forms the basis for many applications such as video scrambling, compression,
and auto-regressive models that are used in generative modeling for images.
Existing algorithms resort to a fixed scanning algorithm such as Raster scan or
Hilbert scan. Instead, our work learns a spatially coherent linear ordering of
pixels from the dataset of images using a graph-based neural network. The
resulting Neural SFC is optimized for an objective suitable for the downstream
task when the image is traversed along with the scan line order. We show the
advantage of using Neural SFCs in downstream applications such as image
compression. Code and additional results will be made available at
https://hywang66.github.io/publication/neuralsfc
Studies on image compression and image reconstruction
During this six month period our works concentrated on three, somewhat different areas. We looked at and developed a number of error concealment schemes for use in a variety of video coding environments. This work is described in an accompanying (draft) Masters thesis. In the thesis we describe application of this techniques to the MPEG video coding scheme. We felt that the unique frame ordering approach used in the MPEG scheme would be a challenge to any error concealment/error recovery technique. We continued with our work in the vector quantization area. We have also developed a new type of vector quantizer, which we call a scan predictive vector quantization. The scan predictive VQ was tested on data processed at Goddard to approximate Landsat 7 HRMSI resolution and compared favorably with existing VQ techniques. A paper describing this work is included. The third area is concerned more with reconstruction than compression. While there is a variety of efficient lossless image compression schemes, they all have a common property that they use past data to encode future data. This is done either via taking differences, context modeling, or by building dictionaries. When encoding large images, this common property becomes a common flaw. When the user wishes to decode just a portion of the image, the requirement that the past history be available forces the decoding of a significantly larger portion of the image than desired by the user. Even with intelligent partitioning of the image dataset, the number of pixels decoded may be four times the number of pixels requested. We have developed an adaptive scanning strategy which can be used with any lossless compression scheme and which lowers the additional number of pixels to be decoded to about 7 percent of the number of pixels requested! A paper describing these results is included
Image Compression Using Cascaded Neural Networks
Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented
Image Compression Using Cascaded Neural Networks
Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented
Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization
The confocal microscopy imaging techniques, which allow optical sectioning, have
been successfully exploited in biomedical studies. Biomedical scientists can benefit
from more realistic visualization and much more accurate diagnosis by processing and
analysing on a three-dimensional image data. The lack of efficient image compression
standards makes such large volumetric image data slow to transfer over limited
bandwidth networks. It also imposes large storage space requirements and high cost in
archiving and maintenance.
Conventional two-dimensional image coders do not take into account inter-frame
correlations in three-dimensional image data. The standard multi-frame coders, like
video coders, although they have good performance in capturing motion information,
are not efficiently designed for coding multiple frames representing a stack of optical
planes of a real object. Therefore a real three-dimensional image compression
approach should be investigated.
Moreover the reconstructed image quality is a very important concern in compressing
medical images, because it could be directly related to the diagnosis accuracy. Most of
the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete-
wavelet-transform (DWT). However in DCT and DWT methods, the control
of the reconstructed image quality is inconvenient, involving considerable costs in
computation, since they are fundamentally rate-parameterized methods rather than
distortion-parameterized methods. Therefore it is very desirable to develop a
transform-based distortion-parameterized compression method, which is expected to
have high coding performance and also able to conveniently and accurately control
the final distortion according to the user specified quality requirement.
This thesis describes our work in developing a distortion-constraint three-dimensional
image compression approach, using vector quantization techniques combined with
image pyramid structures. We are expecting our method to have:
1. High coding performance in compressing three-dimensional microscopic
image data, compared to the state-of-the-art three-dimensional image coders
and other standardized two-dimensional image coders and video coders.
2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical
image compression applications, is superior to the rate-parameterized methods
in achieving a user specified quality requirement.
The result is a three-dimensional image compression method, which has outstanding
compression performance, measured objectively, for volumetric microscopic images.
The distortion-constraint feature, by which users can expect to achieve a target image
quality rather than the compressed file size, offers more flexible control of the
reconstructed image quality than its rate-constraint counterparts in medical image
applications. Additionally, it effectively reduces the artifacts presented in other
approaches at low bit rates and also attenuates noise in the pre-compressed images.
Furthermore, its advantages in progressive transmission and fast decoding make it
suitable for bandwidth limited tele-communications and web-based image browsing
applications
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