Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia ...
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Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT,which are commonly employed in diverse application areas, especially forcapturing remote sensing (RS) images. At the same time, the interpretationof the captured RS image also plays a crucial issue, which can be addressedby the multi-label classification and Computational Linguistics based imagecaptioning techniques. To achieve this, this paper presents an efficient lowcomplexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves thelow complexity encoder using the Neighborhood Correlation Sequence (NCS)with a burrows wheeler transform (BWT) technique called LCE-BWT forencoding the RS images captured by the UAV. The application of NCS greatlyreduces the computation complexity and requires fewer resources for imagetransmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presentedto determine the multiple class labels of the RS image, shows the novelty ofthe work. Finally, the Computational Linguistics based Bidirectional encoderRepresentations from Transformers (BERT) technique is applied for imagecaptioning, to provide a proficient textual description of the RS image. Theperformance of the presented technique is tested using the UCM dataset. Thesimulation outcome implied that the presented model has obtained effectivecompression performance, reconstructed image quality, classification results,and image captioning outcome.
In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal re...
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In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes incoherent linear measurements of the signal, which are represented as separate bitplanes. Each bitplane is coded using a distributed source code of the appropriate rate, and transmitted. The decoder, starts from the least significant biplane and, using a prediction of the signal as side information, iteratively recovers each bitplane based on the source prediction and the signal, assuming all the previous bitplanes of lower significance have already been recovered. We provide theoretical results guiding the rate selection, relying only on the least squares prediction error of the source. This is in contrast to existing approaches which rely on difficult-to-estimate information-theoretic metrics to set the rate. We validate our approach using simulations on remote-sensing multispectral images, comparing them with existing approaches of similar complexity.
Rate-Distortion (RD) performance of Distributed Video Coding (DVC) is considerably less than that of conventional predictive video coding. In order to reduce the performance gap, many methods and techniques have been ...
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Rate-Distortion (RD) performance of Distributed Video Coding (DVC) is considerably less than that of conventional predictive video coding. In order to reduce the performance gap, many methods and techniques have been proposed to improve the coding efficiency of DVC with increased system complexity, especially techniques employed at the encoder such as encoder mode decisions, optimal quantization, hash methods etc., no doubt increase the complexity of the encoder. However, low complexity encoder is a widely desired feature of DVC. In order to improve the coding efficiency while maintaining low complexity encoder, this paper focuses on Distributed Residual Video Coding (DRVC) architecture and proposes a simple encoder scheme. The main contributions of this paper are as follows: 1) propose a bit plane block based method combined with bit plane re-arrangement to improve the dependency between source and Side Information (SI), and meanwhile, to reduce the amount of data to be channel encoded 2) present a simple iterative dead-zone quantizer with 3 levels in order to adjust quantization from coarse to fine. The simulation results show that the proposed scheme outperforms DISCOVER scheme for low to medium motion video sequences in terms of RD performance, and maintains a low complexity encoder at the same time.
An innovative electrocardiogram compression algorithm is presented in this paper. The proposed method is based on matrix completion, a new paradigm in signal processing that seeks to recover a low-rank matrix based on...
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ISBN:
(纸本)9781424441228
An innovative electrocardiogram compression algorithm is presented in this paper. The proposed method is based on matrix completion, a new paradigm in signal processing that seeks to recover a low-rank matrix based on a small number of observations. The low-rank matrix is obtained via normalization of electrocardiogram records. Using matrix completion, the ECG data matrix is recovered from a few number of entries, thereby yielding high compression ratios comparable to those obtained by existing compression techniques. The proposed scheme offers a low-complexityencoder, good tolerance to quantization noise, and good quality reconstruction.
We propose a novel video coding scheme requiring a simple encoder and a complex decoder where video frames are intra-coded periodically and frames in between successive intra-coded frames are coded efficiently using a...
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ISBN:
(纸本)0819456586
We propose a novel video coding scheme requiring a simple encoder and a complex decoder where video frames are intra-coded periodically and frames in between successive intra-coded frames are coded efficiently using a proposed irregular binning technique. We investigate a method of forming an irregular binning which is capable of quantizing any value effectively with only small number of bins, by exploiting the correlation between successive frames. This correlation is additionally exploited at the decoder, where the quality of reconstructed frames is enhanced, gradually by applying POCS (projection on the convex sets). After an image frame is reconstructed with the irregular binning information at the proposed decoder, we can further improve the resulting quality by modifying the reconstructed image with motion-compensated image components from the neighboring frames. In the proposed decoder, several iterations of these modification and re-projection steps can be invoked. Experimental results show that the performance of the proposed coding scheme is comparable to that of H.264/AVC coding in IB mode. Since the proposed video coding does not require motion estimation at the encoder, it can be considered as an alternative for some versions of H.264/AVC in applications requiring a simple encoder.
作者：
Xu, MengWang, YaoNYU
Polytech Inst Dept Elect & Comp Engn New York NY 10003 USA
This paper presents a novel one-pass mode decision algorithm for encoding multiple quality layers, following the coarse grain scalability coding structure in the H.264/SVC standard. In our solution, motion estimation ...
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ISBN:
(纸本)9781479916030
This paper presents a novel one-pass mode decision algorithm for encoding multiple quality layers, following the coarse grain scalability coding structure in the H.264/SVC standard. In our solution, motion estimation (ME) is carried out only once at the base layer using the reconstructed picture from the highest enhancement layer. The same motion vectors are used for all layers to not only avoid multiple ME processes at different layers, but also save the overhead bits. In addition, early SKIP/DIRECT mode decision is introduced to further boost the encoding speed. The encoder produces fully compliant SVC bit streams. Although the method is applicable to both coarse grain scalability (CGS) and medium grain scalability (MGS), we have examined its performance over CGS only. We demonstrate that more than 2x speedup for three-layer coding against the conventional H.264/SVC encoding using the reference software over 7 test sequences. Significantly, this complexity saving is achieved simultaneously with increase in the coding efficiency! Although the base layer requires slightly higher bit rate (2.5% in terms of the BD-Rate), the enhancement layers enjoy lower rates (5.7% and 2.2% reduction for the total of two and three layers, respectively), on average of 7 test sequences.
The paradigm of using a very simple encoder and a sophisticated decoder for compression of signals became popular with the theory of distributed coding and it has been exercised for the compression of various types of...
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ISBN:
(纸本)9781479974504
The paradigm of using a very simple encoder and a sophisticated decoder for compression of signals became popular with the theory of distributed coding and it has been exercised for the compression of various types of signals such as images and video. The theory of compressive sampling later introduced a similar concept but with the focus on guarantees of signal recovery using sparse and low rank priors lying in an incoherent domain to the domain of sampling. In this paper, we bring together the concepts introduced in distributed coding and compressive sampling with the informed source separation, in which the goal is to efficiently compress the audio sources so that they can be decoded with the knowledge of the mixture of the sources. The proposed framework uses a very simple time domain sampling scheme to encode the sources, and a sophisticated decoding algorithm that makes use of the low rank non-negative tensor factorization model of the distribution of short-time Fourier transform coefficients to recover the sources, which is a direct application of the principles of both compressive sampling and distributed coding.
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