147 research outputs found
Peak Transform for Efficient Image Representation and Coding
Digital Object Identifier 10.1109/TIP.2007.896599In this work, we introduce a nonlinear geometric transform, called peak transform (PT), for efficient image representation
and coding. The proposed PT is able to convert
high-frequency signals into low-frequency ones, making them much easier to be compressed. Coupled with wavelet transform
and subband decomposition, the PT is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient
data representation and compression. To maximize the transform coding gain, we develop a dynamic programming solution for
optimum PT design. Based on PT, we design an image encoder, called the PT encoder, for efficient image compression. Our extensive
experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a PT is applied. The
PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in peak signal-to-noise ratio (PSNR), especially for images with a significant amount
of high-frequency components. Our experimental results also show that the proposed PT is able to efficiently capture and preserve high-frequency image features (e.g., edges) and yields significantly improved visual quality
Resource Allocation and Performance Analysis of Wireless Video Sensors
Digital Object Identifier 10.1109/TCSVT.2006.873154Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource
constraints, including limitations with respect to energy supply,on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power,we study the optimum power allocation between video encoding
and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total
power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.This work was supported in part by the National Science Foundation under
Grant DBI-0529082 and Grant DBI-0529012
Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation
One central challenge in source-free unsupervised domain adaptation (UDA) is
the lack of an effective approach to evaluate the prediction results of the
adapted network model in the target domain. To address this challenge, we
propose to explore a new method called cross-inferential networks (CIN). Our
main idea is that, when we adapt the network model to predict the sample labels
from encoded features, we use these prediction results to construct new
training samples with derived labels to learn a new examiner network that
performs a different but compatible task in the target domain. Specifically, in
this work, the base network model is performing image classification while the
examiner network is tasked to perform relative ordering of triplets of samples
whose training labels are carefully constructed from the prediction results of
the base network model. Two similarity measures, cross-network correlation
matrix similarity and attention consistency, are then developed to provide
important guidance for the UDA process. Our experimental results on benchmark
datasets demonstrate that our proposed CIN approach can significantly improve
the performance of source-free UDA.Comment: ICIP2023 accepte
Lossless Image Compression Using Super-Spatial Structure Prediction
Digital Object Identifier 10.1109/LSP.2010.2040925We recognize that the key challenge in image
compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures. In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction. This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure components within previously encoded image regions. We find that this super-spatial prediction is very efficient for image regions with significant structure components. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Given an unknown dynamical system, what is the minimum number of samples
needed for effective learning of its governing laws and accurate prediction of
its future evolution behavior, and how to select these critical samples? In
this work, we propose to explore this problem based on a design approach.
Starting from a small initial set of samples, we adaptively discover critical
samples to achieve increasingly accurate learning of the system evolution. One
central challenge here is that we do not know the network modeling error since
the ground-truth system state is unknown, which is however needed for critical
sampling. To address this challenge, we introduce a multi-step reciprocal
prediction network where forward and backward evolution networks are designed
to learn the temporal evolution behavior in the forward and backward time
directions, respectively. Very interestingly, we find that the desired network
modeling error is highly correlated with the multi-step reciprocal prediction
error, which can be directly computed from the current system state. This
allows us to perform a dynamic selection of critical samples from regions with
high network modeling errors for dynamical systems. Additionally, a joint
spatial-temporal evolution network is introduced which incorporates spatial
dynamics modeling into the temporal evolution prediction for robust learning of
the system evolution operator with few samples. Our extensive experimental
results demonstrate that our proposed method is able to dramatically reduce the
number of samples needed for effective learning and accurate prediction of
evolution behaviors of unknown dynamical systems by up to hundreds of times
- …