6,770 research outputs found
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support
This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices
Multiple description video coding for stereoscopic 3D
In this paper, we propose an MDC schemes for stereoscopic 3D video. In the literature, MDC has previously been applied in 2D video but not so much in 3D video. The proposed algorithm enhances the error resilience of the 3D video using the combination of even and odd frame based MDC while retaining good temporal prediction efficiency for video over error-prone networks. Improvements are made to the original even and odd frame MDC scheme by adding a controllable amount of side information to improve frame interpolation at the decoder. The side information is also sent according to the video sequence motion for further improvement. The performance of the proposed algorithms is evaluated in error free and error prone environments especially for wireless channels. Simulation results show improved performance using the proposed MDC at high error rates compared to the single description coding (SDC) and the original even and odd frame MDC
DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs
In addition to General Purpose Processors (GPP), Multicore SoCs equipping
modern mobile devices contain specialized Digital Signal Processor designed
with the aim to provide better performance and low energy consumption
properties. However, the experimental measurements we have achieved revealed
that system overhead, in case of DSP video decoding, causes drastic
performances drop and energy efficiency as compared to the GPP decoding. This
paper describes DyPS, a new approach for energy-aware processor switching (GPP
or DSP) according to the video quality . We show the pertinence of our solution
in the context of adaptive video decoding and describe an implementation on an
embedded Linux operating system with the help of the GStreamer framework. A
simple case study showed that DyPS achieves 30% energy saving while sustaining
the decoding performanc
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