32 research outputs found
A solvable class of quadratic 0β1 programming
AbstractWe show that the minimum of the pseudo-Boolean quadratic function Ζ(x) = xTQx + cTx can be found in linear time when the graph defined by Q is transformable into a combinatorial circuit of AND, OR, NAND, NOR or NOT logic gates. A novel modeling technique is used to transform the graph defined by Q into a logic circuit. A consistent labeling of the signals in the logic circuit from the set {0, 1} corresponds to the global minimum of Ζ and the labeling is determined through logic simulation of the circuit. Our approach establishes a direct and constructive relationship between pseudo-Boolean functions and logic circuits.In the restricted case when all the elements of Q are nonpositive, the minimum of Ζ can be obtained in polynomial time [15]. We show that the problem of finding the minimum of Ζ, even in the special case when all the elements of Q are positive, is NP-complete
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming
Ensuring high-quality video content for wireless users has become
increasingly vital. Nevertheless, maintaining a consistent level of video
quality faces challenges due to the fluctuating encoded bitrate, primarily
caused by dynamic video content, especially in live streaming scenarios. Video
compression is typically employed to eliminate unnecessary redundancies within
and between video frames, thereby reducing the required bandwidth for video
transmission. The encoded bitrate and the quality of the compressed video
depend on encoder parameters, specifically, the quantization parameter (QP).
Poor choices of encoder parameters can result in reduced bandwidth efficiency
and high likelihood of non-conformance. Non-conformance refers to the violation
of the peak signal-to-noise ratio (PSNR) constraint for an encoded video
segment. To address these issues, a real-time deep learning-based H.264
controller is proposed. This controller dynamically estimates the optimal
encoder parameters based on the content of a video chunk with minimal delay.
The objective is to maintain video quality in terms of PSNR above a specified
threshold while minimizing the average bitrate of the compressed video.
Experimental results, conducted on both QCIF dataset and a diverse range of
random videos from public datasets, validate the effectiveness of this
approach. Notably, it achieves improvements of up to 2.5 times in average
bandwidth usage compared to the state-of-the-art adaptive bitrate video
streaming, with a negligible non-conformance probability below .Comment: arXiv admin note: text overlap with arXiv:2310.0685
Semantic Multi-Resolution Communications
Deep learning based joint source-channel coding (JSCC) has demonstrated
significant advancements in data reconstruction compared to separate
source-channel coding (SSCC). This superiority arises from the suboptimality of
SSCC when dealing with finite block-length data. Moreover, SSCC falls short in
reconstructing data in a multi-user and/or multi-resolution fashion, as it only
tries to satisfy the worst channel and/or the highest quality data. To overcome
these limitations, we propose a novel deep learning multi-resolution JSCC
framework inspired by the concept of multi-task learning (MTL). This proposed
framework excels at encoding data for different resolutions through
hierarchical layers and effectively decodes it by leveraging both current and
past layers of encoded data. Moreover, this framework holds great potential for
semantic communication, where the objective extends beyond data reconstruction
to preserving specific semantic attributes throughout the communication
process. These semantic features could be crucial elements such as class
labels, essential for classification tasks, or other key attributes that
require preservation. Within this framework, each level of encoded data can be
carefully designed to retain specific data semantics. As a result, the
precision of a semantic classifier can be progressively enhanced across
successive layers, emphasizing the preservation of targeted semantics
throughout the encoding and decoding stages. We conduct experiments on MNIST
and CIFAR10 dataset. The experiment with both datasets illustrates that our
proposed method is capable of surpassing the SSCC method in reconstructing data
with different resolutions, enabling the extraction of semantic features with
heightened confidence in successive layers. This capability is particularly
advantageous for prioritizing and preserving more crucial semantic features
within the datasets