44,110 research outputs found
Unequal Error Protected JPEG 2000 Broadcast Scheme with Progressive Fountain Codes
This paper proposes a novel scheme, based on progressive fountain codes, for
broadcasting JPEG 2000 multimedia. In such a broadcast scheme, progressive
resolution levels of images/video have been unequally protected when
transmitted using the proposed progressive fountain codes. With progressive
fountain codes applied in the broadcast scheme, the resolutions of images (JPEG
2000) or videos (MJPEG 2000) received by different users can be automatically
adaptive to their channel qualities, i.e. the users with good channel qualities
are possible to receive the high resolution images/vedio while the users with
bad channel qualities may receive low resolution images/vedio. Finally, the
performance of the proposed scheme is evaluated with the MJPEG 2000 broadcast
prototype
RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding
We present a new hierarchical compression scheme for encoding light field
images (LFI) that is suitable for interactive rendering. Our method (RLFC)
exploits redundancies in the light field images by constructing a tree
structure. The top level (root) of the tree captures the common high-level
details across the LFI, and other levels (children) of the tree capture
specific low-level details of the LFI. Our decompressing algorithm corresponds
to tree traversal operations and gathers the values stored at different levels
of the tree. Furthermore, we use bounded integer sequence encoding which
provides random access and fast hardware decoding for compressing the blocks of
children of the tree. We have evaluated our method for 4D two-plane
parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per
pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR
quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI
are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new
views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to
implement and involves only bit manipulations and integer arithmetic
operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and
Games (I3D '19
Querying XML data streams from wireless sensor networks: an evaluation of query engines
As the deployment of wireless sensor networks increase and their application domain widens, the opportunity for effective use of XML filtering and streaming query engines is ever more present. XML filtering engines aim to provide efficient real-time querying of streaming XML encoded data. This paper provides a detailed analysis of several such engines, focusing on the technology involved, their capabilities, their support for XPath and their performance. Our experimental evaluation identifies which filtering engine is best suited to process a given query based on its properties. Such metrics are important in establishing the best approach to filtering XML streams on-the-fly
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.Comment: 13 figures (includes supplementary information
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