17,426 research outputs found
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wise loss weighted by SSIM increases reconstruction
quality according to several metrics. Second, we modify the recurrent
architecture to improve spatial diffusion, which allows the network to more
effectively capture and propagate image information through the network's
hidden state. Finally, in addition to lossless entropy coding, we use a
spatially adaptive bit allocation algorithm to more efficiently use the limited
number of bits to encode visually complex image regions. We evaluate our method
on the Kodak and Tecnick image sets and compare against standard codecs as well
recently published methods based on deep neural networks
Energy-efficient wireless communication
In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters
Real-time image streaming over a low-bandwidth wireless camera network
In this paper we describe the recent development of a low-bandwidth wireless camera sensor network. We propose a simple, yet effective, network architecture which allows multiple cameras to be connected to the network and synchronize their communication schedules. Image compression of greater than 90% is performed at each node running on a local DSP coprocessor, resulting in nodes using 1/8th the energy compared to streaming uncompressed images. We briefly introduce the Fleck wireless node and the DSP/camera sensor, and then outline the network architecture and compression algorithm. The system is able to stream color QVGA images over the network to a base station at up to 2 frames per second. © 2007 IEEE
Wavelet Based Image Coding Schemes : A Recent Survey
A variety of new and powerful algorithms have been developed for image
compression over the years. Among them the wavelet-based image compression
schemes have gained much popularity due to their overlapping nature which
reduces the blocking artifacts that are common phenomena in JPEG compression
and multiresolution character which leads to superior energy compaction with
high quality reconstructed images. This paper provides a detailed survey on
some of the popular wavelet coding techniques such as the Embedded Zerotree
Wavelet (EZW) coding, Set Partitioning in Hierarchical Tree (SPIHT) coding, the
Set Partitioned Embedded Block (SPECK) Coder, and the Embedded Block Coding
with Optimized Truncation (EBCOT) algorithm. Other wavelet-based coding
techniques like the Wavelet Difference Reduction (WDR) and the Adaptive Scanned
Wavelet Difference Reduction (ASWDR) algorithms, the Space Frequency
Quantization (SFQ) algorithm, the Embedded Predictive Wavelet Image Coder
(EPWIC), Compression with Reversible Embedded Wavelet (CREW), the Stack-Run
(SR) coding and the recent Geometric Wavelet (GW) coding are also discussed.
Based on the review, recommendations and discussions are presented for
algorithm development and implementation.Comment: 18 pages, 7 figures, journa
Development and evaluation of packet video schemes
Reflecting the two tasks proposed for the current year, namely a feasibility study of simulating the NASA network, and a study of progressive transmission schemes, are presented. The view of the NASA network, gleaned from the various technical reports made available to use, is provided. Also included is a brief overview of how the current simulator could be modified to accomplish the goal of simulating the NASA network. As the material in this section would be the basis for the actual simulation, it is important to make sure that it is an accurate reflection of the requirements on the simulator. Brief descriptions of the set of progressive transmission algorithms selected for the study are contained. The results available in the literature were obtained under a variety of different assumptions, not all of which are stated. As such, the only way to compare the efficiency and the implementational complexity of the various algorithms is to simulate them
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Emulating spiking neural networks on analog neuromorphic hardware offers
several advantages over simulating them on conventional computers, particularly
in terms of speed and energy consumption. However, this usually comes at the
cost of reduced control over the dynamics of the emulated networks. In this
paper, we demonstrate how iterative training of a hardware-emulated network can
compensate for anomalies induced by the analog substrate. We first convert a
deep neural network trained in software to a spiking network on the BrainScaleS
wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10
000 compared to the biological time domain. This mapping is followed by the
in-the-loop training, where in each training step, the network activity is
first recorded in hardware and then used to compute the parameter updates in
software via backpropagation. An essential finding is that the parameter
updates do not have to be precise, but only need to approximately follow the
correct gradient, which simplifies the computation of updates. Using this
approach, after only several tens of iterations, the spiking network shows an
accuracy close to the ideal software-emulated prototype. The presented
techniques show that deep spiking networks emulated on analog neuromorphic
devices can attain good computational performance despite the inherent
variations of the analog substrate.Comment: 8 pages, 10 figures, submitted to IJCNN 201
Data compression and computational efficiency
In this thesis we seek to make advances towards the goal of effective learned compression. This entails using machine learning models as the core constituent of compression algorithms, rather than hand-crafted components.
To that end, we first describe a new method for lossless compression. This method
allows a class of existing machine learning models – latent variable models – to be
turned into lossless compressors. Thus many future advancements in the field of
latent variable modelling can be leveraged in the field of lossless compression. We
demonstrate a proof-of-concept of this method on image compression. Further, we
show that it can scale to very large models, and image compression problems which
closely resemble the real-world use cases that we seek to tackle.
The use of the above compression method relies on executing a latent variable
model. Since these models can be large in size and slow to run, we consider how
to mitigate these computational costs. We show that by implementing much of the
models using binary precision parameters, rather than floating-point precision, we
can still achieve reasonable modelling performance but requiring a fraction of the
storage space and execution time.
Lastly, we consider how learned compression can be applied to 3D scene data - a
data medium increasing in prevalence, and which can require a significant amount of
space. A recently developed class of machine learning models - scene representation
functions - has demonstrated good results on modelling such 3D scene data. We
show that by compressing these representation functions themselves we can achieve
good scene reconstruction with a very small model size
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