84,855 research outputs found
Weighted universal image compression
We describe a general coding strategy leading to a family of universal image compression systems designed to give good performance in applications where the statistics of the source to be compressed are not available at design time or vary over time or space. The basic approach considered uses a two-stage structure in which the single source code of traditional image compression systems is replaced with a family of codes designed to cover a large class of possible sources. To illustrate this approach, we consider the optimal design and use of two-stage codes containing collections of vector quantizers (weighted universal vector quantization), bit allocations for JPEG-style coding (weighted universal bit allocation), and transform codes (weighted universal transform coding). Further, we demonstrate the benefits to be gained from the inclusion of perceptual distortion measures and optimal parsing. The strategy yields two-stage codes that significantly outperform their single-stage predecessors. On a sequence of medical images, weighted universal vector quantization outperforms entropy coded vector quantization by over 9 dB. On the same data sequence, weighted universal bit allocation outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed test and image data, weighted universal transform coding outperforms a single, data-optimized transform code (which gives performance almost identical to that of JPEG) by over 6 dB
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Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
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
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
Personal photos of individuals when shared online, apart from exhibiting a
myriad of memorable details, also reveals a wide range of private information
and potentially entails privacy risks (e.g., online harassment, tracking). To
mitigate such risks, it is crucial to study techniques that allow individuals
to limit the private information leaked in visual data. We tackle this problem
in a novel image obfuscation framework: to maximize entropy on inferences over
targeted privacy attributes, while retaining image fidelity. We approach the
problem based on an encoder-decoder style architecture, with two key novelties:
(a) introducing a discriminator to perform bi-directional translation
simultaneously from multiple unpaired domains; (b) predicting an image
interpolation which maximizes uncertainty over a target set of attributes. We
find our approach generates obfuscated images faithful to the original input
images, and additionally increase uncertainty by 6.2 (or up to 0.85
bits) over the non-obfuscated counterparts.Comment: 20 pages, 7 figure
Optimal quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks
Recently, an image scrambling encryption algorithm of pixel bit based on
chaos map was proposed. Considering the algorithm as a typical binary image
scrambling/permutation algorithm exerting on plaintext of size ,
this paper proposes a novel optimal method to break it with some
known/chosen-plaintexts. The spatial complexity and computational complexity of
the attack are only and respectively,
where is the number of known/chosen-plaintexts used. The method can be
easily extended to break any permutation-only encryption scheme exerting on
plaintext of size and with different levels of values. The
corresponding spatial complexity and computational complexity are only
and respectively. In addition, some
specific remarks on the performance of the image scrambling encryption
algorithm are presented.Comment: 11 pages, 6 figure
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