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The surge in ultra-high-definition video content has intensified the demand for advanced video compression techniques. Video encoding preprocessing can improve coding efficiency while ensuring a high degree of compatibility with existing codecs. Existing video encoding preprocessing methods are limited in their ability to fully exploit redundant features in video data and recover high-frequency details, and their network architectures often lack compatibility with neural video encoders. To addressing these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method to improve the efficiency of deep learning-based neural video encoders. Firstly, our approach integrates a degradation compensation module to mitigate encoding noise and boost feature extraction efficiency. Secondly, a lightweight fully convolutional neural network is employed, which utilizes residual learning and knowledge distillation to refine and suppress irrelevant features across spatial and channel dimensions. Furthermore, to maximize the use of redundant information, we incorporate Dense Blocks, which can enhance and reconstruct important features in the video data during preprocessing. Finally, the preprocessed frames are then mapped from pixel space to feature space through the Dense Feature-Enhanced Video Compression (DFVC) module, which improves motion estimation and compensation accuracy. The experimental results demonstrate that, compared to neural video encoders, the MDER method can reduce bits per pixel (Bpp) by 0.0714 and 0.0536 under equivalent PSNR and MS-SSIM conditions, respectively. These results demonstrate significant improvements in compression efficiency and reconstruction quality, highlighting the effectiveness of the MDER preprocessing method and its compatibility with neural video codec workflows. [ABSTRACT FROM AUTHOR]

In the video compression industry, video compression tailored to machine vision tasks has recently emerged as a critical area of focus. Given the unique characteristics of machine vision, the current practice of directly employing conventional codecs reveals inefficiency, which requires compressing unnecessary regions. In this paper, we propose a framework that more aptly encodes video regions distinguished by machine vision to enhance coding efficiency. For that, the proposed framework consists of deep learning-based adaptive switch networks that guide the efficient coding tool for video encoding. Through the experiments, it is demonstrated that the proposed framework has superiority over the latest standardization project, video coding for machine benchmark, which achieves a Bjontegaard delta (BD)-rate gain of 5.91% on average and reaches up to a 19.51% BD-rate gain. [ABSTRACT FROM AUTHOR]

The rail running band is a mathematical representation describing the continuous strip-shaped spatial surface resulting from the rolling contact operation of train wheels on the rail surface, which establishes a direct mapping relationship with the wheel–rail interaction, and the nature of this interaction significantly influences the safety and comfort of train operations. Therefore, accurate detection of the running band is crucial for enhancing the safety and comfort of train travel. Traditional running band detection relies on manual inspection methods, utilizing a scale for measurements on the rail. However, this approach is characterized by high labor costs, slow detection speeds, and a lack of systematic data preservation. This paper proposes R2Bnet, a lightweight semantic segmentation algorithm that achieves pixel-level detection of rail running bands. R2Bnet is an enhanced encoder-decoder architecture built upon ShuttleNet. Different from ShuttleNet, R2Bnet optimizes the number of repetitive codecs in ShuttleNet and redesigns the encoder's residual structure to match the unique characteristics of rail running bands, allowing the backbone network to effectively capture long-range dependencies. Furthermore, R2Bnet integrates an efficient channel attention mechanism to enhance focus on critical regions and optimize feature representations. The F -measure and mean intersection over union (mIOU) achieved by R2Bnet on 300 testing images were 98.47% and 0.9617, respectively. Notably, R2Bnet outperformed six state-of-the-art models for semantic segmentation and demonstrated a significant 39% improvement in speed compared with the average speed of the six networks provided. [ABSTRACT FROM AUTHOR]

The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets. [ABSTRACT FROM AUTHOR]

A tunable active-noise-control (ANC) circuit topology for headsets used in different applications is presented in this article. In the current consumer headset market, ANC is a mature technology that is commonly applied to wireless headsets connected to smartphones for listening to music and making phone calls. The development of ANC headsets has resulted in low-cost and simple devices due to the built-in ANC registers in the digital circuit. Digital circuit implementation is important to provide ultra-low latency processing for different algorithms compared with using digital signal processors (DSPs). However, a limitation associated with these built-in ANC filters is that the ANC digital circuit has been designed only for the wireless headset consumer market. Consequently, ANC headsets can only be used for designated features. ANC tuned for planes by eliminating low-frequency engine noise has been commonly used in the past twenty years. Pass-through (PT) amplification is also commonly tuned to allow users to hear external sounds and have conversations without having to remove their headset. In addition, ANC and PT amplification are also not allowed to operate simultaneously on the existing chipsets. In this paper, an ANC circuit topology with ultra-low latency processing is presented, where the ANC response of the headset can be tuned without any restriction imposed by the built-in ANC filters. The proposed ANC circuit topology consists of a commercial ANC chipset with an external audio codec to provide tunable characteristics. It is achieved by fixed maximum noise cancellation of ANC chipset being modified by summing with the signal from audio-codec path via an audio mixer. Acoustic performance for several headset applications implemented using the proposed circuit is validated. This simple circuit topology can be easily developed and adapted to the current headset consumer market for consumer-selective ANC in different environments. [ABSTRACT FROM AUTHOR]

3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method "CPCC-PES" that focuses on point cloud's edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments [ABSTRACT FROM AUTHOR]

It is challenging to enable fast channel switching and packet-loss repair in low-delay live video distribution without negatively influencing the steady-state viewing performance. For example, regularly breaking the inter-frame dependency by introducing intra-predicted keyframes enables random access, but is costly in terms of rate-distortion performance. For this reason, the keyframe-injection method minimizes the impact by sending a compression-efficient normal video stream to all end-users. As accompaniment, a companion stream that solely consists of keyframes is sporadically used for only those users that switch channels or experience packet loss. This paper describes the requirements to implement keyframe injection in three video coding standard generations (H.264/AVC, H.265/HEVC, and H.266/VVC). We evaluated the impact that keyframe injection has on the quality of the video in terms of a decrease in VMAF, PSNR and SSIM. We demonstrate that the quality reduction caused by keyframe insertion is generally low, meaning that keyframe injection typically is imperceptible. However, drift-error artifacts become perceptible over time for rare outliers. Moreover, we pinpointed the cause of this worst-case artifact type to be halfpel interpolation. As a solution, codecs can disable subpel motion estimation, and future standards could design their filters more carefully. Lastly, it should be noted that keyframe injection will only be applied sporadically when users require a random access or experience packet loss, and only to those users. Most interestingly, all other users receive a compression-efficient stream wherein the inter-frame dependency is not artificially broken at regular short intervals. As such, our proposed solution makes low-latency video distribution efficient and viable in multiple coding standards. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications. [ABSTRACT FROM AUTHOR]

This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy. [ABSTRACT FROM AUTHOR]

Over the years, there has been growing interest in multimedia services, especially in the video domain, where firms and subscribers require higher resolutions, framerates, and sampling precision. This results in a huge amount of data that needs to be processed, stored, and transmitted. As a result, researchers face the challenge of developing new compression standards that can reduce the amount of data while maintaining the same quality. In this paper, the compression performance of the latest and most commonly used video codecs, namely H.266/VVC, AV1, H265/HEVC, and H.264/AVC was examined. The test set included seven sequences of various content at 8K, Ultra HD (UHD), and Full HD (FHD) resolutions, encoded to bitrates ranging from 1 to 15 Mbps for FHD and UHD resolutions and from 5 to 50 Mbps for 8K resolution. Objective quality metrics, such as peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and video multi-method assessment fusion (VMAF) were used to measure codec performance. The results showed that H.266/VVC outperformed all other codecs, namely H.264/AVC, H.265/HEVC, and AV1, in terms of the Bjøntegaard delta (BD) model. The average bitrate savings were approximately 78% for H.266/VVC, 63% for AV1, and 53% for H.265/HEVC relative to H.264/AVC, 59% for H.266/VVC and 22% for AV1 compared to H.264/AVC, and 46% for H.266/VVC relative to AV1 (all for 8K resolution). The results also showed that codec performance varied depending on resolution, with higher resolutions showing greater efficiency for newly developed codecs, such as H.266/VVC and AV1. This confirms the fact that the H.266/VVC and AV1 codecs were primarily developed for videos at high resolutions, such as 8K and/or UHD. [ABSTRACT FROM AUTHOR]

Voice over internet protocol (VoIP) calls are increasingly transported over computer-based networking due to several factors, such as low call rates. However, point-to-point (P-P) calls, as a division of VoIP, are encountering a capacity utilization issue. The main reason for that is the giant packet header, especially when compared to the runt P-P calls packet payload. Therefore, this research article introduced a method to solve the liability of the giant packet header of the P-P calls. The introduced method is named voice segment compaction (VSC). The VSC method employs the unneeded P-P calls packet header elements to carry the voice packet payload. This, in turn, reduces the size of the voice payload and improves network capacity utilization. The preliminary results demonstrated the importance of the introduced VSC method, while network capacity improved by up to 38.33%. [ABSTRACT FROM AUTHOR]

Recently, machine learning-based image compression has attracted increasing interests and is approaching the state-of-the-art compression ratio. But unlike traditional codec, it lacks a universal optimization method to seek efficient representation for different images. In this paper, we develop a plug-and-play optimization framework for seeking higher compression ratio, which can be flexibly applied to existing and potential future compression networks. To make the latent representation more efficient, we propose a novel latent optimization algorithm to adaptively remove the redundancy for each image. Additionally, inspired by the potential of side information for traditional codecs, we introduce side information into our framework, and integrate side information optimization with latent optimization to further enhance the compression ratio. In particular, with the joint side information and latent optimization, we can achieve fine rate control using only single model instead of training different models for different rate-distortion trade-offs, which significantly reduces the training and storage cost to support multiple bit rates. Experimental results demonstrate that our proposed framework can remarkably boost the machine learning-based compression ratio, achieving more than 10% additional bit rate saving on three different representative network structures. With the proposed optimization framework, we can achieve 7.6% bit rate saving against the latest traditional coding standard VVC on Kodak dataset, yielding the state-of-the-art compression ratio. [ABSTRACT FROM AUTHOR]

Video coding for machines (VCM) is a rapidly growing field dedicated to bridging the gap between video and feature coding. For storage-intensive aerial videos, VCM offers valuable insights into a more efficient coding paradigm. However, the frequent occurrence of small objects poses a challenge to VCM, with limited distinctive features and inherent distortion in the reconstructed videos. To address this issue, we propose small object-aware VCM (SOAVCM), a joint video and feature coding approach that handles small objects. Particularly, the video coding incorporates a feature-guided residual (FGR) codec to preserve the small objects, utilizing features obtained from feature coding. Simultaneously, feature coding employs the motion vector (MV) estimated in video coding to generate compact high-level features. By leveraging the inherent synergy between features and MVs, SOAVCM significantly enhances overall coding efficiency. Experimental results demonstrate that SOAVCM outperforms several deep-learning-based methods and traditional coding standards in video coding. Moreover, the encoded feature representation improves detection accuracy and achieves substantial bitrate savings. [ABSTRACT FROM AUTHOR]

A 32-bit floating-point format is often used for the development and training of deep neural networks. Training and inference in deep learning-optimized codecs can result in enormous performance and energy efficiency advantages. However, training and inferring low-bit neural networks still pose a significant challenge. In this study, we propose a sorting method that maintains accuracy in numerical formats with a low number of bits. We tested this method on convolutional neural networks, including AlexNet. Using our method, we found that in our convolutional neural network, the accuracy achieved with 11 bits matches that of the IEEE 32-bit format. Similarly, in AlexNet, the accuracy achieved with 10 bits matches that of the IEEE 32-bit format. These results suggest that the sorting method shows promise for calculations with limited accuracy. [ABSTRACT FROM AUTHOR]

Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the availability of subjective ratings. When contents of a light field dataset are visualized on a light field display, the display system matches the received input to its output capabilities through various processes, such as interpolation. Therefore, one of the most straightforward methods to create light field contents for a specific display is to consider its visualization parameters during acquisition. In this paper, we introduce a novel display-specific light field dataset, captured using a DSLR camera and a turntable rig. The visual data of the seven static scenes were recorded twice by using two settings of angular resolution. While both were acquired uniformly within a 53-degree angle, which matches the viewing cone of the display they were captured for, one dataset consists of 70 views per content, while the other of 140. Capturing the contents twice was a more straightforward solution than downsampling, as the latter approach could either degrade the quality or make the FOV size inaccurate. The paper provides a detailed characterization of the captured contents, as well as compressed variations of the contents with various codecs, together with the calculated values of commonly-used quality metrics for the compressed light field contents. We expect that this dataset will be useful for the research community working on light field compression, processing, and quality assessment, for instance to perform subjective quality assessment tests on a display with a 53-degree display cone and to test new interpolation methods and objective quality metrics. In future work, we will also focus on subjective tests and provide relevant results. This dataset is made free to access for the research community. [ABSTRACT FROM AUTHOR]

High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB. [ABSTRACT FROM AUTHOR]

Abstract This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy.

Abstract In recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods.

In recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods. [ABSTRACT FROM AUTHOR]

This article presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection.Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. [ABSTRACT FROM AUTHOR]

This paper is a part of our contribution to research on the enhancement of network automatic speech recognition system performance. We built a highly configurable platform by using hidden Markov models, Gaussian mixture models, and Mel frequency spectral coefficients, in addition to VoIP G.711-u and GSM codecs. To determine the optimal values for maximum performance, different acoustic models are prepared by varying the hidden Markov models (from 3 to 5) and Gaussian mixture models (8–16-32) with 13 feature extraction coefficients. Additionally, our generated acoustic models are tested by unencoded and encoded speech data based on G.711 and GSM codecs. The best parameterization performance is obtained for 3 HMM, 8–16 GMMs, and G.711 codecs. [ABSTRACT FROM AUTHOR]

It is challenging to find a proper way to compress computer-generated holography (CGH) data owing to their huge data requirements and characteristics. This study proposes CGH data coding systems with high-efficiency video coding (HEVC), three-dimensional extensions of HEVC (3D-HEVC), and video-based point cloud compression (V-PCC) codecs. In the proposed system, we implemented a procedure for codec usage and format conversion and evaluated the objective and subjective results to analyze the performance of the three coding systems. We discuss the relative advantages and disadvantages of the three coding systems with respect to their coding efficiency and reconstruction results. Our analysis concluded that 3D-HEVC and V-PCC are potential solutions for compressing red, green, blue, and depth (RGBD)-sourced CGH data. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.

Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the lossless compression efficiency for high spatial resolution microarray images using general entropy codecs, namely Huffman and arithmetic coders and the image compression standard JPEG 2000. Using the standards ensures that decoders are available to reassess the images for future applications. Typically, microarray images have a bit-depth of 16. In proposed algorithm 1, every image's per bit-plane entropy profile is calculated to automatically determine a better threshold T to split the bit-planes into the foreground and background sub-images. T is initially set to 8. However, in algorithm 1, T is updated, balancing the average value of per bit-plane entropies of the segmented sub-images of an image for improved lossless compression results. Codecs are applied individually to the produced sub-images. Proposed algorithm 2 is designed to increase the lossless compression efficiency of any unmodified JPEG 2000-compliant encoder while reducing side information overhead. In this, pixel intensity reindexing and, thereby, changing the histograms of the same segmented sub-images obtained from algorithm 1 are implemented and confirmed to get better JPEG 2000 results in lossless mode than applying it to the original image. The lossless JPEG 2000 compression performance on microarray images is also compared to JPEG-LS in particular. The experiments are carried out to validate the methods on seven benchmark datasets, namely ApoA1, ISREC, Stanford, MicroZip, GEO, Arizona, and IBB. The average first-order entropy of the datasets above is calculated and compared for codecs and better than competitive efforts in the literature. [ABSTRACT FROM AUTHOR]

Voice over Internet Protocol (VoIP) is simply the transport of voice traffic over the Internet or any IP-based packet-switched network. This is in contrast with the traditional telephone network, carrying voice data over dedicated circuit-switched transmission lines. A major challenge to implementing VoIP is to ensure sufficient bandwidth, and access to the available bandwidth is controlled and prioritized. Sufficient bandwidth is required to maintain highquality voice. On the other hand, controlling it limits bandwidth-hogging applications and guarantees access to delay-sensitive applications. The objective of this paper is to familiarize the readers with building a test VoIP lab setup using open-source tools; experimenting how different network impairments would affect VoIP quality; and explaining the lab setup activities, investigations, data analysis and research. Subsequently, the paper gives an overview of various technicalities such as operating system, servers, codec selection, signaling protocols, etc. involved in achieving network quality-of-service. [ABSTRACT FROM AUTHOR]

Reliability of Static Random Access Memories (SRAMs) is immensely affected by radiation induced soft errors. Multiple Cell Upsets (MCUs) are caused by these soft errors and this lead to data corruption in several SRAM cells. Error Correcting Codes are normally exploited to alleviate the effect of MCUs. Single Error Correction–Double Error Detection (SEC–DED) codes are not the appropriate preference against MCUs rather these codes are suitable for protecting SRAM cells against Single Cell Upset. Single Error Correction–Double Error Detection–Double Adjacent Error Correction (SEC–DED–DAEC) codes are more suitable due to increasing trends of single and double-adjacent errors in SRAM cells. In this paper, a new SEC–DED–DAEC code has been proposed which is proficient of correcting single and double-adjacent errors in SRAMs. Our newly proposed code has been termed as Single and Double-adjacent Error Correcting Code (SDECC). Proposed codecs have been designed by employing new parity check matrices for different word lengths which are frequently applied in memory. The mis-correction probability of proposed SDECC codes are up to 88.24% and 80.67% lesser for 2-random and 3-random bit errors respectively with respect to related existing designs. Proposed codecs have been replicated and synthesized in ASIC environment. It is observed that area and power consumption of the proposed codecs are reduced up to 8.93% and 21.47% compared to the recently published results respectively. [ABSTRACT FROM AUTHOR]

The occurrences of Multiple Cell Upset (MCU) are more liable to arise in modern memory systems with the continuous upgradation of microelectronics technology from micron to deep submicron scales. These MCUs are mainly induced due to radiations in memory systems. Error Correcting Codes (ECCs) with lower design complexity are generally preferred for the mitigation of MCUs. The major drawback of the existing ECC is requiring higher overheads as error correction capability increases. In this paper, authors have proposed a new class of high performance t -bit Burst Error Correcting (BEC) codes. Parity check matrices (H) have been proposed for 3-bit and 4-bit BEC codes with word lengths of 16-bit, 32-bit and 64-bit. Also a simplified decoding scheme has been introduced for these codes. The proposed codecs have been designed and implemented in FPGA and ASIC platforms. The proposed codecs are compact in area, faster in speed and efficient in power compared to existing related schemes. But these lower design constrains are achieved at the cost of increase in redundancy. So, the proposed codecs can be employed in applications where redundancy is not the only constrain for correcting t -bit burst errors caused by MCUs. [ABSTRACT FROM AUTHOR]

Voice over IP (VoIP) is widely utilized by organizations, schools, colleges, and so on. Nevertheless, VoIP numerous challenges that hinder its spread. One of the significant challenges is the poor exploit of the VoIP technology network bandwidth (BW), caused by the huge preamble of the VoIP packet. This paper suggests a novel methodology to manage this huge preamble overhead challenge. The proposed methodology is named runt payload VoIP packet (RPV). The core principle of the RPV methodology is to reemploy and exploit the VoIP packet preamble’s data (fields) that are superfluous by VoIP technology, especially for unicast IP voice calls. Generally, those fields will be used to convey the VoIP packet payload. Consequently, diminish or zero the length of the payload and, therefore, spare the BW. The results of the investigation into the suggested RPV methodology indicated significant enhancement in the BW exploitation of VoIP technology. For instance, the saved BW in the examined environment with the LPC codec came to up to 25.9%.

The new event cameras are now widely used in many computer vision applications. Their high raw data bitrate levels require a more efficient fixed-length representation for low-bandwidth transmission from the event sensor to the processing chip. A novel low-complexity lossless compression framework is proposed for encoding the synchronous event frames (EFs) by introducing a novel memory-efficient fixed-length representation suitable for hardware implementation in the very-low-power (VLP) event-processing chip. A first contribution proposes an improved representation of the ternary frames using pixel-group frame partitioning and symbol remapping. Another contribution proposes a novel low-complexity memory-efficient fixed-length representation using multi-level lookup tables (LUTs). Complex experimental analysis is performed using a set of group-size configurations. For very-large group-size configurations, an improved representation is proposed using a mask-LUT structure. The experimental evaluation on a public dataset demonstrates that the proposed fixed-length coding framework provides at least two times the compression ratio relative to the raw EF representation and a close performance compared with variable-length video coding standards and variable-length state-of-the-art image codecs for lossless compression of ternary EFs generated at frequencies bellow one KHz. To our knowledge, the paper is the first to introduce a low-complexity memory-efficient fixed-length representation for lossless compression of synchronous EFs, suitable for integration into a VLP event-processing chip. [ABSTRACT FROM AUTHOR]

The Joint Video Exploration Team (JVET) has created the Versatile Video Coding Standard (VVC/H.266), the most up-to-date video coding standard, offering a broad selection of coding tools. The maturity of commercial VVC codecs can significantly reduce costs and improve coding efficiency. However, the latest video coding standards have introduced binomial and trinomial tree partitioning methods, which cause the coding units (CUs) to have various shapes, increasing the complexity of coding. This article proposes a technique to simplify VVC intra prediction through the use of gradient analysis and a multi-feature fusion CNN. The gradient of CUs is computed by employing the Sobel operator, the calculation results are used for predecision-making. Further decisions can be made by CNN for coding units that cannot be judged whether they should be segmented or not. We calculate the standard deviation (SD) and the initial depth as the input features of the CNN. To implement this method, the initial depth can be determined by constructing a segmented depth prediction dictionary. For the initial segmentation depth of the coding unit, regardless of its shape, it can also be determined by consulting the dictionary. The algorithm can determine whether to split CUs of varying sizes, decreasing the complexity of the CU division process and making VVC more practical. Experimental results demonstrate that the proposed algorithm can reduce encoding time by 36.56% with a minimal increase of 1.06% Bjøntegaard delta bit rate (BD-BR) compared to the original algorithm. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) image reconstruction is emerging as a leading challenge for 3D media on the Internet and virtual reality. In this regard, compression performance in 3D technology is one of the most important issues. Various codecs such as HEVC and AV1 have been suggested to improve compression performance in 3D technology. In this study, a hybrid method based on AV1 codec combined with mathematical methods is proposed for improving the quality of this codec. In the proposed method, two AV1 compression steps are used to estimate the AV1 codec error using a linear relationship and added to the basic codec output to improve the compression performance. The proposed method shows better quality in the Cartesian form (real and imaginary parts) of the hologram in Holo and Object plane when compared to new codecs that have been proposed in the field of 3D compression. In addition, the proposed method can be used as a general compression method for 2D images. Based on simulation results, the proposed method improved the quality of reconstructed hologram by up to 63% and 5 dB in terms of BDRate and BDPSNR, respectively. [ABSTRACT FROM AUTHOR]

This paper is dedicated to video coding based on a compressive sensing (CS) framework. In CS, it is assumed that if a video sequence is sparse in some transform domain, then it could be reconstructed from a much lower number of samples (called measurements) than the Nyquist–Shannon theorem requires. Here, the performance of such a codec depends on how the measurements are acquired (or sensed) and compressed and how the video is reconstructed from the decoded measurements. Here, such a codec potentially could provide significantly faster encoding compared with traditional block-based intra-frame encoding via Motion JPEG (MJPEG), H.264/AVC or H.265/HEVC standards. However, existing video codecs based on CS are inferior to the traditional codecs in rate distortion performance, which makes them useless in practical scenarios. In this paper, we present a video codec based on CS called CS-JPEG. To the author's knowledge, CS-JPEG is the first codec based on CS, combining fast encoding and high rate distortion results. Our performance evaluation shows that, compared with the optimized software implementations of MJPEG, H.264/AVC, and H.265/HEVC, the proposed CS-JPEG encoding is 2.2, 1.9, and 30.5 times faster, providing 2.33, 0.79, and 1.45 dB improvements in the peak signal-to-noise ratio, respectively. Therefore, it could be more attractive for video applications having critical limitations in computational resources or a battery lifetime of an upstreaming device. [ABSTRACT FROM AUTHOR]

The developments in smartphones, high data rates, and substantial video data traffic have increased the burden on cellular networks. Consequently, this burden significantly affects the Quality of Experience of the cellular users leading to an increased network delay for the diverse video content requests. To accommodate the requests from different users with varying requirements, one of the promising solutions is to cache videos in the near vicinity of users and transcode them online. The online transcoding is performed at the edge level of the cellular network to minimize the network delay and use the bandwidth efficiently. However, the feasibility of online transcoding is significantly affected by various factors, such as the codecs, configurations of virtual machines, the cost incurred, and estimated time to complete the transcoding task, among other parameters. Although online transcoding is discussed in the literature adequately, few studies discuss the feasibility of online transcoding while considering all the aforementioned critical parameters. This study examined the effects of a diverse range of critical parameters on the feasibility of online transcoding. We performed extensive simulations on the local machine environment to study various possible factors affecting online transcoding in detail. We then transcoded the same videos on Amazon Elastic Cloud Computing (EC2) Virtual Machines (VMs) to further study realistic cloud settings with fine-tuned configurations. Our experiments show the superior performance of some codecs and the effects of machine configurations on transcoding tasks duration. We aim to provide a benchmark for practitioners and researchers considering online transcoding in real-time multimedia applications. [ABSTRACT FROM AUTHOR]

Modern codecs remove temporal redundancy of a video via inter prediction, i.e., searching previously coded frames for similar blocks and storing motion vectors to save bit-rates. However, existing codecs adopt blocklevel motion estimation, where a block is regressed by reference blocks linearly and is doomed to fail to deal with non-linear motions. In this article, we generate virtual reference frames (VRFs) with previously reconstructed frames via deep networks to offer an additional candidate, which is not constrained to linear motion structure and further significantly improves coding efficiency. More specifically, we propose a novel deep Auto-Regressive Moving-Average (ARMA) model, Error-Corrected Auto-Regressive Network (ECARNet), equipped with the powers of the conventional statistic ARMA models and deep networks jointly for reference frame prediction. Similar to conventionalARMAmodels, the ECAR-Net consists of two stages:Auto- Regression (AR) stage and Error-Correction (EC) stage, where the first part predicts the signal at the current time-step based on previously reconstructed frames, while the second one compensates for the output of the AR stage to obtain finer details. Different from the statistic AR models only focusing on short-term temporal dependency, the AR model of our ECAR-Net is further injected with the long-term dynamics mechanism, where long temporal information is utilized to help predict motions more accurately. Furthermore, ECAR-Net works in a configuration-adaptive way, i.e., using different dynamics and error definitions for the Low Delay B and Random Access configurations, which helps improve the adaptivity and generality in diverse coding scenarios. With the well-designed network, our method surpasses HEVC on average 5.0% and 6.6% BD-rate saving for the luma component under the Low Delay B and Random Access configurations and also obtains on average 1.54% BD-rate saving over VVC. Furthermore, ECAR-Net works in a configuration-adaptive way, i.e., using different dynamics and error definitions for the Low Delay B and Random Access configurations, which helps improve the adaptivity and generality in diverse coding scenarios. [ABSTRACT FROM AUTHOR]

Forensic examination of digital audio, video, and images frequently requires transforming multimedia data from one format to another. The transformative activity may cause changes to the administrative elements of the file but leave the multimedia streams unchanged and intact. However, the forensic science community has a method knowledge gap in accurately determining if the multimedia streams changed or remained the same in the transformative processes. This paper illustrates the practical use of multimedia stream hashing as a forensic method for verifying multimedia content. A universal stream hashing tool decodes the multimedia stream data at rest in a file container. Subsequently, it calculates the data stream hash using reference video, audio, and image codecs. This paper illustrates that the multimedia stream hashing method can accurately confirm the integrity of digital images, videos, and audio following transmission, transcoding, or re‐containerization. Our findings confirmed that stream hashing could accurately detect changes in multimedia streams during transcoding. Furthermore, the stream hashing method can also accurately detect matching multimedia streams. In addition, this paper verified the forensic use of the multimedia stream hash method while establishing the error rate for its use. The hash algorithms used in stream hashing have zero false negative rates by design. However, the false positive (error rate) is extremely low and depends on hashing algorithm. Finally, we recommend the forensic science community adopt the multimedia stream hashing method as an initial testing method. The method can verify a multimedia stream's conversion (transcoding) from one codec to another using FFmpeg. Highlights: Re‐encoding of multimedia streams must be transparent for forensic examinations.Multimedia stream hashing is a stream integrity verification method used in forensic science.Multimedia stream hashing has an extremely low potential for a false positive (error rate). [ABSTRACT FROM AUTHOR]

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Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of ${\mathbb {F}}_{q^{2}}^{*}$. To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection. [ABSTRACT FROM AUTHOR]

An improved Singleton-type upper bound is presented for the list decoding radius of linear codes, in terms of the code parameters $[n,k,d]$ and the list size $L$. $L$ -MDS codes are then defined as codes that attain this bound (under a slightly stronger notion of list decodability), with 1-MDS codes corresponding to ordinary linear MDS codes. Several properties of such codes are presented; in particular, it is shown that the 2-MDS property is preserved under duality. Finally, explicit constructions for 2-MDS codes are presented through generalized Reed–Solomon (GRS) codes. [ABSTRACT FROM AUTHOR]

The binary quadratic-residue codes and the punctured Reed-Muller codes ${\mathcal {R}}_{2}((m-1)/2, m))$ are two families of binary cyclic codes with parameters $[n, (n+1)/2, d \geq \sqrt {n}]$. These two families of binary cyclic codes are interesting partly due to the fact that their minimum distances have a square-root bound. The objective of this paper is to construct two families of binary cyclic codes of length $2^{m}-1$ and dimension near $2^{m-1}$ with good minimum distances. When $m \geq 3$ is odd, the codes become a family of duadic codes with parameters $[2^{m}-1, 2^{m-1}, d]$ , where $d \geq 2^{(m-1)/2}+1$ if $m \equiv 3 \pmod {4}$ and $d \geq 2^{(m-1)/2}+3$ if $m \equiv 1 \pmod {4}$. The two families of binary cyclic codes contain some optimal binary cyclic codes. [ABSTRACT FROM AUTHOR]

Twisted generalized Reed-Solomon (TGRS) codes are a family of codes that contains a large number of maximum distance separable (MDS) codes that are non-equivalent to generalized Reed-Solomon (GRS) codes. In this paper, we characterize a sufficient and necessary condition that a twisted Reed-Solomon (TRS) code with two twists is MDS; give a sufficient and necessary condition that a TGRS code with two twists is self-dual, and present some constructions of self-dual TGRS codes. These self-dual codes are MDS, NMDS or 2-MDS. Furthermore, we study the non-GRS properties of TGRS codes with two twists and prove that these codes are non-GRS in most cases. [ABSTRACT FROM AUTHOR]

Streaming rendered 3D content over a network to a thin client device, such as a phone or a VR/AR headset, brings high-fidelity graphics to platforms where it would not normally possible due to thermal, power, or cost constraints. Streamed 3D content must be transmitted with a representation that is both robust to latency and potential network dropouts. Transmitting a video stream and reprojecting to correct for changing viewpoints fails in the presence of disocclusion events; streaming scene geometry and performing high-quality rendering on the client is not possible on limited-power mobile GPUs. To balance the competing goals of disocclusion robustness and minimal client workload, we introduce QuadStream, a new streaming content representation that reduces motion-to-photon latency by allowing clients to efficiently render novel views without artifacts caused by disocclusion events. Motivated by traditional macroblock approaches to video codec design, we decompose the scene seen from positions in a view cell into a series of quad proxies, or view-aligned quads from multiple views. By operating on a rasterized G-Buffer, our approach is independent of the representation used for the scene itself; the resulting QuadStream is an approximate geometric representation of the scene that can be reconstructed by a thin client to render both the current view and nearby adjacent views. Our technical contributions are an efficient parallel quad generation, merging, and packing strategy for proxy views covering potential client movement in a scene; a packing and encoding strategy that allows masked quads with depth information to be transmitted as a frame-coherent stream; and an efficient rendering approach for rendering our QuadStream representation into entirely novel views on thin clients. We show that our approach achieves superior quality compared both to video data streaming methods, and to geometry-based streaming. [ABSTRACT FROM AUTHOR]

Speech coding has been shown to achieve good speech quality using either waveform matching or parametric reconstruction. For very low bit rate streams, recently developed generative speech models can reconstruct high‐quality wideband speech from the bit streams of standard parametric encoders at less than 3 kb/s. Generative codecs produce high‐quality speech based on synthesising speech from a DNN and the parametric input. Existing objective speech quality models (e.g., ViSQOL and POLQA) cannot be used to accurately evaluate the quality of coded speech from generative models as they penalise based on signal differences not apparent in subjective listening test results. This paper presents WARP‐Q, a full‐reference objective speech quality metric that uses a dynamic time warping cost for MFCC representations of the signals. It is robust to low perceptual signal changes introduced by low bit rate neural vocoders. An evaluation using waveform matching, parametric, and generative neural vocoder‐based codecs as well as channel and environmental noise shows that WARP‐Q has better correlation and codec quality ranking for novel codecs compared to traditional metrics as well as the versatility of capturing other types of degradations, such as additive noise and transmission channel degradations. [ABSTRACT FROM AUTHOR]