Facts compression and source coding techniques are used in Wi-Fi networks for several packages. Those techniques may be used to lessen the size of transmitted information, thereby increasing the spectral performance o...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Facts compression and source coding techniques are used in Wi-Fi networks for several packages. Those techniques may be used to lessen the size of transmitted information, thereby increasing the spectral performance of a community and improving the performance of the channel. In addition, fact compression can also be used for programs such as video streaming and voice verbal exchange. Records compression and supply coding strategies are often utilized in Wi-Fi networks to reduce the bit fee of a channel. By using efficient algorithms, facts may be compressed and transmitted without sacrificing first-rate. Compression algorithms can be used to reduce the facts fee and throughput of a wireless network without decreasing the best of the records. Through doing so, it is feasible to reduce the size of transmitted data while growing the performance and performance of the channel. Source coding is another technique used in wireless networks to optimize the performance of the channel. Supply coding algorithms are used to lessen the bit charge of a channel with the aid of the use of various techniques together with bitsensible coding and entropy coding. Those algorithms can assist in lessening the c of records that are dispatched over the air and can also help lessen the noise components. Statistics compression and supply coding can also be used to improve the performance of video streaming and voice communiqué applications in a Wi-Fi network.
Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce po...
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Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses asymmetric numeral systems- an innovative approach to entropy coding which can be used for compression with encryption. It provides a compression ratio comparable with arithmetic coding at a similar speed as Huffman coding;hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the asymmetric numeral system makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing an additional encryption layer when reaching a satisfactory protection level.
Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This wo...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive coding, Run Length coding and Quantum entropy coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming.
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression p...
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ISBN:
(数字)9798331534714
ISBN:
(纸本)9798331534721
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression framework, namely DeepFGS. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. For entropy coding, we design a mutual entropy model to fully explore the correlation between the basic and scalable features. In addition, we reuse the decoder to reduce the parameters and computational complexity. Experiments demonstrate that our proposed DeepFGS outperforms previous learning-based scalable image compression models and traditional scalable image codecs in both PSNR and MS-SSIM metrics.
Range coding is a type of entropy coding widely used in modern data compressors. However, its compression and decompression processes involve multiple range adjustments, and the bitstream can only be read sequentially...
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ISBN:
(数字)9798331534714
ISBN:
(纸本)9798331534721
Range coding is a type of entropy coding widely used in modern data compressors. However, its compression and decompression processes involve multiple range adjustments, and the bitstream can only be read sequentially during decoding, resulting in quite high latency. In addition, existing input-interleaved fast implementations demand additional computational and memory overhead for the post-compression byte-swizzling step, which leads to increased compression time. In this paper, we propose a parallel range coding method that employs multiple encoders and decoders without the need for the swizzling step. It is achieved by designing a sliding window mechanism to interleave the outputs of multiple encoders, so that the positions of each encoder's outputs in the bitstream follow a predictable and ordered pattern. This design reduces encoding latency and enables each decoder to pre-locate the data it needs to read during decoding, thereby improving both compression and decompression performance. The simulation results indicate that compared to the traditional range coding and existing multi-way input-interleaved implementations (which require a large amount of memory overhead during encoding), our proposal achieves an average throughput increase of 48.03%/81.08% and 74.01%/9.14% during encoding/decoding, respectively, with almost the same compression ratios.
Implicit Neural Representation (INR) has introduced a novel paradigm for image compression, achieving competitive Rate-Distortion (RD) performance with low decoding complexity. Existing INR-based codecs typically comp...
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ISBN:
(数字)9798331534714
ISBN:
(纸本)9798331534721
Implicit Neural Representation (INR) has introduced a novel paradigm for image compression, achieving competitive Rate-Distortion (RD) performance with low decoding complexity. Existing INR-based codecs typically comprise three core components: (1) Multilayer Perceptron (MLP) networks, (2) an entropy coding module, and (3) a set of latent grids. Encoding a specific image involves overfitting these components to the image. However, current approaches often initiate overfitting from scratch, utilizing random or zero-initialized parameters. This approach necessitates tens of minutes to several hours for full overfitting, rendering it highly inefficient and impractical. To address this limitation, we propose MLIIC: a Meta-Learned Implicit Image Codec built upon the state-of-the-art INR-based image codec. Our enhanced meta-learning methodology provides a generalizable initialization that reduces baseline encoding time by an order of magnitude. Empirical results demonstrate that MLIIC not only achieves more than 15 × faster encoding speed but also exhibits superior RD performance compared to baseline initialized with random or zero parameters.
Sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicl...
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ISBN:
(数字)9798331534714
ISBN:
(纸本)9798331534721
Sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicles and infrastructure to achieve superior analysis performance compared to single-vehicle approaches. However, due to the limited bandwidth of V2X communication links, directly transmitting features can be inefficient, resulting in significant delays that are unacceptable for real-time decision-making. To address this challenge, we propose a compact feature representation method in the bird's eye view (BEV) space for communication-efficient collaborative analysis. As shown in Fig. 1, the proposed method can be viewed as a task-aware distributed coding approach with decoder side information. First, the ego vehicle and the networked infrastructure convert raw LiDAR data into BEV features using a shared PointPillars feature extractor. The infrastructure then applies the proposed BEV codec to transform these BEV features into a compact representation, encoding them into a binary bitstream through entropy coding based on the estimated distribution. The received features are subsequently warped and fused with the ego vehicle's features using a bidirectional attention fusion module, and processed by a single-shot detector to perform 3D object detection. Experimental results on the DAIR-V2X-C dataset demonstrate that the proposed framework achieves more than 1000 times compression compared to directly transmitting floating-point features, while maintaining high analysis performance in real-world V2X scenarios.
This paper presents a picture partitioning design of Neural Network-based Intra coding (NNIC) for Video coding for Machines (VCM). The proposed design introduces adaptive auto-encoder and probability model processing,...
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ISBN:
(数字)9798350349399
ISBN:
(纸本)9798350349405
This paper presents a picture partitioning design of Neural Network-based Intra coding (NNIC) for Video coding for Machines (VCM). The proposed design introduces adaptive auto-encoder and probability model processing, and a new unit for the partitions of a NNIC picture. In conscious with the causality of the transmission order of the partitions, the adaptive auto-encoder processing exploits more the correlations of pixel values around the partition boundaries than a conventional design. Therefore it can bring coding gains while keeping low-delay video transmission capability. The adaptive probability model processing allows both encoder and decoder to start their entropy coding and decoding with the same delay as the conventional design. The new unit makes the picture partition signaling of NNIC compatible with that of Versatile Video coding (VVC) forming the inner video coding of VCM. Simulation results show that, compared to the conventional, the proposal can attain the bit rate reduction of 11% on average with respect to machine tasks.
Generalized models dominate neural video compression methods to compress arbitrary video with the same model. However, obtaining a universal neural codec with high compression efficiency on all videos is challenging. ...
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ISBN:
(数字)9798331529543
ISBN:
(纸本)9798331529550
Generalized models dominate neural video compression methods to compress arbitrary video with the same model. However, obtaining a universal neural codec with high compression efficiency on all videos is challenging. Existing work attempts to adapt the decoder-side model per video content through full parameter tuning, but this requires a large Group of Pictures (GOP) to compensate for the cost of transmitting updated parameters. To tackle this challenge, we propose to tune generalized video codecs per video content in a parameter-efficient manner. The per-content tuned parameters are further compressed with entropy coding using adaptive distribution estimations. This allows for enhancing the compression efficiency while maintaining a normal GOP size for random access capabilities. To validate the generality and validity of our approach, we apply it to two representative methods: CNN-based, DCVC-HEM, and Transformer-based, VCT. Our results demonstrate that introducing content-specific representation leads to a notable improvement in compression efficiency compared to the original methods.
A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS rec...
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ISBN:
(纸本)9798400716256
A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the time, location, and polarity of brightness change. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Despite yielding a lower bit rate compared to conventional video capture, the present approach of event capture demonstrates enhanced compressibility. Hence, we introduce a novel deep learning-based compression methodology tailored for event data. The proposed technique employs a deep belief network (DBN) to condense the high-dimensional event data into a latent representation, which is subsequently encoded utilising an entropy-based coding method. Notably, our proposed scheme represents one of the initial endeavours to integrate deep learning methodologies for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques.
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