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Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions. In this paper, we aim to fill in the gap and design a Chinese fake audio detection dataset (CFAD) for studying more generalized detection methods. Twelve mainstream speech-generation techniques are used to generate fake audio. To simulate the real-life scenarios, three noise datasets are selected for noise adding at five different signal-to-noise ratios, and six codecs are considered for audio transcoding (format conversion). CFAD dataset can be used not only for fake audio detection but also for detecting the algorithms of fake utterances for audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging. The CFAD dataset is publicly available. 1 1 https://zenodo.org/record/8122764. • We construct a standard public Chinese dataset CFAD for fake audio detection studies under complex conditions. • CFAD dataset considers 12 types of fake audio under noisy conditions and transcoding (format conversion) conditions. • Detailed and sufficient baselines of fake audio detection and fake algorithm recognition are provided. • CFAD is publicly available (https://zenodo.org/record/8122764). • Generalization studies across unseen types, mismatched background noise and media codecs are supported. [ABSTRACT FROM AUTHOR]

Just noticeable distortion (JND), reflecting the perceptual redundancy directly, has been widely used in image and video compression. However, the human visual system (HVS) is extremely complex and the visual signal processing has not been fully understood, which result in existing JND models are not accurate enough and the bitrate saving of JND-based perceptual compression schemes is limited. This paper presents a novel pixel-based JND model for videos and a JND-based perceptual quantization scheme for HEVC codecs. In particular, positive and negative perception effects of the inter-frame difference and the motion information are analyzed and measured with an information-theoretic approach. Then, a surprise-based JND model is developed for perceptual video coding (PVC). In our PVC scheme, the frame-level perceptual quantization parameter (QP) is derived on the premise that the coding distortion is infinitely close to the estimated JND threshold. On the basis of the frame-level perceptual QP, we determine the perceptual QP for each coding unit through a perceptual adjustment function to achieve better perceptual quality. Experimental results indicate that the proposed JND model outperforms existing models significantly, the proposed perceptual quantization scheme improves video compression efficiency with better perceptual quality and lower coding complexity. • This paper presented a novel pixel-based JND model for videos considering positive and negative perception effects. • This paper proposed an information-theoretic approach to analyze and measure the perception effects of the inter-frame difference and the motion information. • This paper designed a perceptual quantization scheme for HEVC codecs to determine the perceptual quantization parameter of each coding unit. [ABSTRACT FROM AUTHOR]

This paper considers the problem of temporal prediction for inter-frame coding of video sequences using locally linear embedding (LLE). LLE-based prediction, first considered for intra-frame prediction, computes the predictor as a linear combination of K nearest neighbors ( K -NN) searched within one or several reference frames. The paper explores different K -NN search strategies in the context of temporal prediction, leading to several temporal predictor variants. The proposed methods are tested as extra inter-frame prediction modes in an H.264 codec, but the proposed concepts are still valid in HEVC. The results show that significant rate-distortion performance gains are obtained with respect to H.264 (up to 15.31% bit-rate saving). [ABSTRACT FROM AUTHOR]

• Speech spectral sparsity can be exploited by parametric, harmonic beamformers. • These can achieve high noise reduction without compromising signal distortion. • Exploiting speech models also lead to substantial speech dereverberation. • The parametric beamformers has traditional beamformers as special cases. • In adverse conditions, the parametric beamformers improve enhancement considerably. This paper presents a framework for parametric broadband beamforming that exploits the frequency-domain sparsity of voiced speech to achieve more noise reduction than traditional nonparametric broadband beamforming without introducing additional distortion. In this framework, the harmonic model is used to parametrize the signal of interest by a single parameter, the fundamental frequency, whereby both speech enhancement and derevereration can be performed. This framework thus exploits both the spatial and temporal properties of speech signals simultaneously and includes both fixed and adaptive beamformers, such as (1) delay-and-sum, (2) null forming, (3) Wiener, (4) minimum variance distortionless response (MVDR), and (5) linearly constrained minimum variance beamformers. Moreover, the framework contains standard broadband beamforming as a special case, whereby the proposed beamformers can also handle unvoiced speech. The reported experimental results demonstrate the capabilities of the proposed framework to perform both speech enhancement and dereverberation simultaneously. The proposed beamformers are evaluated in terms of speech distortion and objective measures for speech quality and speech intelligibility, and are compared to nonparametric broadband beamformers. The results show that the proposed beamformers perform well compared to traditional methods, including a state-of-the-art dereverberation method, particularly in adverse conditions with high amounts of noise and reverberation. [ABSTRACT FROM AUTHOR]

Inter-wire coupling capacitances can lead to crosstalk fault that is strongly dependent on the transition patterns appearing on the wires. These transition patterns can cause mutual influences between adjacent wires of NoCs and as a result threaten the reliability of data transfer seriously. To increase the reliability of NoCs against the crosstalk fault, Forbidden Pattern Free (FPFs) codes are used. To generate FPF codes, numerical systems are among the overhead-efficient mechanisms. The algorithms of numerical systems have direct effect on the amounts of the codec overheads including power consumption, area occupation and performance of NoCs. To find an overhead-efficient numerical system, this paper proposes an Algorithm for Generating FPF Numerical systems (AGFN). AGFN can provide a tradeoff for designers in selecting overhead-efficient FPF numerical systems. Using this algorithm, collection of Fibonacci-based and non-Fibonacci-based numerical systems can be generated. Then, using AGFN, non-Fibonacci-based FPF numerical system called Summation-based-Subtracted-Added-Penultimate (S2AP) is generated. Experimental results indicate that S2AP not only reduces the worst crosstalk effects by completely removing the Triplet Opposite Direction (TOD) transitions, but also it can significantly improve power consumption, area occupation and critical path overheads of codec with respect to the other state-of-the-art FPF codes. [ABSTRACT FROM AUTHOR]

Predictive Motion Estimation (ME) techniques are fast ME algorithms that have been widely used in video CODECs due to their performance efficiency and low computational complexity. In this paper, a new approach named Optimized Predictive Zonal Search (OPZS) is proposed, which improves the state-of-the-art Enhanced Predictive Zonal Search (EPZS) algorithm. The new initial predicted motion vectors employ fewer, but more optimized predictors, as well as improve the termination procedure. The proposed OPZS not only achieves better output quality and significantly higher compression ratio, but also reduces the number of search points and ME processing time even further. Simulation results after encoding 6 different video test sequences with OPZS along with EPZS and three other algorithms implemented under the same conditions has shown up to 33% compression ratio enhancement. This is achieved by searching up to 14% less points, which means less computational complexity and up to 0.4 dB higher PSNR values. The simplicity and high performance of OPZS make it more practical for implementation on both hardware and software platforms. Since OPZS provides a combination of low computational complexity and high quality encoded images, it is energy efficient and suitable for portable video processing in computation- or power-constrained applications. Furthermore, because of the higher compression ratio this newly proposed algorithm is more compatible with limited capacity storage media and transmission networks with limited band-width. [ABSTRACT FROM AUTHOR]

Achieving high image quality is an important aspect in an increasing number of wireless multimedia applications. These applications require resource efficient error correction hardware to detect and correct errors introduced by the communication channel. This paper presents an innovative flexible architecture for error correction using Low-Density Parity-Check (LDPC) codes. The proposed partially-parallel decoder architecture utilizes a novel code construction technique based on multi-level Hierarchical Quasi-Cyclic (HQC) matrix. The proposed architecture is resource efficient, provides scalable throughput and requires substantially less power compared to other decoders reported to date. The proposed decoder has been implemented on a Xilinx FPGA suitable for WiMAX application and achieves a throughput of 548 Mbps. Performance evaluation of the decoder has been carried out by transmitting JPEG images over a wireless noisy channel and comparing the quality of the reconstructed images with those from other similar decoders. [ABSTRACT FROM AUTHOR]

Abstract: Research on multimedia data coding is allowing for better performance in terms of compression ratio, coding features, and robustness against transmission errors. While rate-distortion performance is being improved at a slower pace if compared to what we were used to up to a decade ago, remarkable advances are being made by adding complex features, such as fast adaptive transforms, lossy to lossless coding, compressed domain processing, etc. One of the latest codec which is expected to improve on the state of the art is the WebP algorithm released by Google. In this paper we provide an objective evaluation of WebP, by comparing it with the JPEG family algorithms. From the results it appears that the performance of the proposed codec is in line with that of the alternative methods, without achieving any major improvement and lacking several features. [Copyright &y& Elsevier]

Abstract: Perception-oriented video coding based on image analysis and completion (IAC) has received increasing attention over recent years. Several approaches have been proposed that follow diverging strategies: from full compatibility to hybrid block transform coding to alternative codec designs. In this paper, we review the most significant issues in IAC coding and their relevance for future codec designs. The most promising pathways will be analyzed along with limitations and open issues. The challenges in IAC-based video coding will be summarized using methods developed by the authors and others for block-based and region-based IAC coding. [Copyright &y& Elsevier]

Abstract: Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos to end clients are targeted toward the H.264/AVC standard or, if scalability is required, the H.264/SVC. In this paper we will introduce a mapping of the MC-EZBC bitstream to existing H.264/SVC based streaming and scaling protocols. This enables the use of highly scalable wavelet based codecs on the one hand and the utilization of already existing network technologies without accruing high implementation costs on the other hand. Furthermore, we will evaluate different scaling options in order to choose the best option for given requirements. Additionally, we will evaluate different encryption options based on transport and bitstream encryption for use cases where digital rights management is required. [Copyright &y& Elsevier]

The advent of new technologies such as high dynamic range or 8K screens has enhanced the quality of digital images but it has also increased the codecs' computational demands to process such data. This paper presents a video codec that, while providing the same coding features and performance as those of JPEG2000, can process 16K video in real time using a consumer-grade GPU. This high throughput is achieved with a technique that introduces complexity scalability to a bitplane coding engine, which is the most computationally complex stage of the coding pipeline. The resulting codec can trade throughput for coding performance depending on the user's needs. Experimental results suggest that our method can double the throughput achieved by CPU implementations of the recently approved High-Throughput JPEG2000 and by hardwired implementations of HEVC in a GPU. [ABSTRACT FROM AUTHOR]

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain. • An improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. • Improved autoencoder architecture. [ABSTRACT FROM AUTHOR]