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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]

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]

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]

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]