Phase-modulated continuous-wave (PMCW) radar is a promising technology for internet of things (IoT) applications due to its high resilience against interference and compatibility with communication systems. To address the computational burden in PMCW radar, conventional approaches accumulate sequences in the time domain to reduce the number of samples used for Doppler processing in velocity estimation. However, the direct summation of sequences without phase compensation leads to severe performance degradation caused by the Doppler effect, including velocity ambiguity and signal-to-noise ratio loss. Therefore, this paper proposes a low-complexity sequence accumulation method for detecting fast-moving targets. By adjusting the desired velocity search region, the proposed method simultaneously resolves the velocity ambiguity and reduces computational complexity. Simulation results show that the proposed method achieves a processing time approximately 3.5 times faster than the non-accumulation method. In addition, the proposed method compensates for the Doppler phase difference along the slow-time to enable coherent accumulation over sequences, thereby preventing performance degradation and achieving higher detection probability compared to conventional accumulation methods.

In this paper, we propose a method to mitigate interference from phase-modulated continuous-wave (PMCW) signals in frequency-modulated continuous-wave (FMCW) systems. In dense automotive sensing environments, PMCW signals enter FMCW receivers and produce broadband distortions after dechirping, which degrades target detectability. To address this issue, we exploit the distinct characteristics of the FMCW signal and the PMCW-induced interference. Specifically, the FMCW signal exhibits a low-rank property in the Hankel domain, whereas the PMCW-induced interference spreads over multiple scales in the wavelet domain due to its chip-wise phase modulation. Based on these properties, we formulate an optimization problem that enforces a low-rank representation for the FMCW signal and applies wavelet-domain regularization to represent the PMCW-induced interference. To efficiently solve this problem, we adopt a solver based on the alternating direction method of multipliers. This solver updates the FMCW signal under a low-rank constraint in the Hankel domain and separates the PMCW-induced interference in the wavelet domain. Simulation results demonstrate that the proposed method outperforms conventional interference mitigation methods, with significant noise floor reduction in the range–velocity map and improved target detectability. As a result, the proposed method achieves a signal-to-interference-plus-noise ratio of 13.151 dB and a correlation coefficient of 0.9651 under strong interference.

We propose an artificial intelligence (AI)-based interference mitigation method to enable the coexistence of phase-modulated continuous wave (PMCW) and orthogonal frequency-division multiplexing (OFDM) systems in spectrum-sharing internet of things (IoT) environments. Existing mitigation techniques struggle to address the complex mutual interference arising from the structural mismatches of heterogeneous systems in dense IoT deployments. To overcome this challenge, we first formulate a signal model that accounts for PMCW–OFDM coexistence and characterizes bidirectional interference between the two systems. Subsequently, we propose a U-Net-based deep learning model that first applies time-domain thresholding as a pre-processing step. The model uses complex-valued inputs and an atrous spatial pyramid pooling bottleneck to capture multi-scale contextual features in the latent space, where ambiguous structures caused by interference can be distinguished from true target components. The proposed model is trained across various vehicular scenarios to enhance robustness under varying interference conditions. Simulation results for both systems under diverse vehicular scenarios demonstrate that the proposed method achieves signal-to-interference-plus-noise ratio improvements of up to 14.47 dB and masked peak signal-to-noise ratio improvements of up to 22.19 dB across various signal-to-noise ratios, compared to conventional baselines such as denoising convolutional neural network (DnCNN) and denoising diffusion probabilistic model (DDPM). In addition, the proposed method reduces latency by 12.5% and 58.7% compared to DnCNN and DDPM, respectively, indicating improved computational efficiency. These results highlight its potential for reliable sensing and offer spatial awareness to facilitate coexistence strategies in large-scale IoT networks.

In this paper, we present a direction of arrival (DOA) estimation method for multiple-input and multiple-output frequency-modulated continuous wave radar systems considering range-angle migration and piecewise sub-array model. In general, DOA estimation methods based on an exact wavefront model can achieve highly accurate results. However, the methods also entail considerable computational complexity, hindering real-time radar data processing in automotive driving. To reduce the computational complexity caused from the nonlinearity of wavefront curvature, we use a piecewise sub-array model and FFT-based methods in both the range and angle domains. The proposed method combines the advantages of far-field-based methods and near-field-based methods, thereby offering enhanced resolution at a reduced computational cost. We confirm that the DOA spectrum generated by the proposed method achieves higher resolution compared to conventional far-field-based methods and demonstrates approximately four times greater computational efficiency (in terms of FLOPs) than the back-projection algorithm.

Phase modulated continuous wave (PMCW) radar is one of promising options for the enhanced sensing capability and the multiple access availability. In this paper, we focus on the advantage of the high-resolution multi-input multi-output (MIMO) radar with fast-time code division multiplexing (CDM). First, we formulate the signal model of the fast-time CDM-MIMO PMCW radar by assigning different code sequences to each transmitter (Tx). We investigate the level of self-code interference induced by simultaneously transmitting Tx signals, as a function of the number of Tx and the type of sequence. For self-code interference cancellation, we propose compressive sensing-based greedy algorithms with significantly reduced computation complexity using the property of the circulant matrix. We show via simulation that the proposed methods can effectively mitigate self-code interference and generate radar images by implementing virtual antenna array with significantly enhanced peak to sidelobe ratio and range/angle estimation accuracy. Since the proposed method can fully leverage the advantages of CDM whose transmit signals share time-frequency resources, it can be utilized for high density Internet of Things networks and increase the fundamental performance of sensing while the reduced computations are suitable for the real-time operation.

In a quadrature receiver, mismatch between the I and Q channels and DC offsets result in I/Q imbalance of the received signal. The I/Q imbalance creates an image target in the signal spectrum and can cause false alarms at the receiver. Therefore, I/Q imbalance needs to be corrected for reliable target detection. This paper proposes an effective method for correcting I/Q imbalance in an frequency modulated continuous wave radar system. The proposed method is based on geometric sequence decomposition of the received signal and can be applied regardless of the number of targets. In addition, a target pairing method utilizing the phase information is presented. The simulation results demonstrate that the proposed method can successfully reconstruct the signals distorted by I/Q imbalance. After the reconstruction of the signal, the image target was eliminated and target information was correctly estimated.

• A large-scale JND-based coded video quality dataset is presented. • The VideoSet contains 220 5-s sequences in four resolutions coded by H.264/AVC. • The subjective test procedure, JND data cleaning and properties are described. • The significance and implications of the VideoSet are discussed. • This work points out a clear path to data-driven perceptual coding. A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed in Lin et al. (2015). Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California in Jin et al. (2016) and Wang et al. (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920 × 1080 , 1280 × 720 , 960 × 540 and 640 × 360 ). For each of the 880 video clips, we encode it using the H.264/AVC codec with QP = 1 , … , 51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (Wang et al., 2016 [4]).

This paper is devoted to Sample Adaptive Offset (SAO). This technique was recently added into High Efficiency Video Coding (HEVC) standard. The concept of SAO is to reduce sample distortion of a region by classifying the region samples into multiple categories, obtaining an offset for each category, and then adding the offset to each sample, where the classifier index and the offsets are coded in the bit stream.

Coding efficiency gains in the High Efficiency Video Coding (H.265/HEVC) standard are achieved by improving many aspects of the traditional hybrid coding framework. Motion compensated prediction, and in particular the interpolation filter, is one of the areas that was improved significantly over H.264/AVC. This paper presents the details of the motion compensation interpolation filter design of the H.265/HEVC standard and its improvements over the interpolation filter design of H.264/AVC. These improvements include discrete cosine transform based filter coefficient design, utilizing longer filter taps for luma and chroma interpolation and using higher precision operations in the intermediate computations. The computational complexity of HEVC interpolation filter is also analyzed both from theoretical and practical perspectives. Experimental results show that a 4.5% average bitrate reduction for the luma component and 13.0% average bitrate reduction for the chroma components are achieved compared to interpolation filter of H.264/AVC. The coding efficiency gains are significant for some video sequences and can reach up to 21.7%.

This paper introduces inter-layer filters for the scalable extension of High Efficiency Video Coding (SHVC) standard, which is being developed by the Joint Collaborative Team on Video Coding (JCT-VC). The major new coding tool in SHVC is inter-layer texture prediction. It provides about 18% average BD-rate reduction compared with HEVC two-layer simulcast. In the case of spatial scalability, base layer reconstructed pictures are up-sampled to the enhancement layer resolution to generate inter-layer texture prediction. A set of 2D separable 8 taps (luma) and 4 taps (chroma) DCT based interpolation filters, which follow the design principles of HEVC motion compensation interpolation filter, are used in the up-sampling process. In the case of SNR scalability, the up-sampling process is not needed since the reference layer has the same spatial resolution as the current layer but encoded with lower quality. This paper proposes a novel inter-layer filter with denoising effect for SNR scalability to improve enhancement layer coding efficiency and equalize the number of stages in inter-layer processing between SNR and spatial scalabilities. Experimental results show that the usage of inter-layer de-noising filter in SNR scalability provides up to 7.5% BD-rate reduction and has observable improvement on subjective visual quality.

Entropy coding is the main important part of all advanced video compression schemes. Context-adaptive binary arithmetic coding (CABAC) is entropy coding used in H.264/MPEG-4 AVC and H.265/HEVC standards. Probability estimation is the key factor of CABAC performance efficiency. In this paper high accuracy probability estimation for CABAC is presented. This technique is based on multiple estimations using different models. Proposed method was efficiently realized in integer arithmetic. High precision probability estimation for CABAC provides up-to 1,4% BD-rate gain.

This paper provides a technical overview of a newly added in-loop filtering technique, sample adaptive offset (SAO), in High Efficiency Video Coding (HEVC). The key idea of SAO is to reduce sample distortion by first classifying reconstructed samples into different categories, obtaining an offset for each category, and then adding the offset to each sample of the category. The offset of each category is properly calculated at the encoder and explicitly signaled to the decoder for reducing sample distortion effectively, while the classification of each sample is performed at both the encoder and the decoder for saving side information significantly. To achieve low latency of only one coding tree unit (CTU), a CTU-based syntax design is specified to adapt SAO parameters for each CTU. A CTU-based optimization algorithm can be used to derive SAO parameters of each CTU, and the SAO parameters of the CTU are inter leaved into the slice data. It is reported that SAO achieves on average 3.5% BD-rate reduction and up to 23.5% BD-rate reduction with less than 1% encoding time increase and about 2.5% decoding time increase under common test conditions of HEVC reference software version 8.0.

High Efficiency Video Coding (HEVC) is the latest joint standardization effort of ITU-T WP 3/16 and ISO/IEC JTC 1/SC 29/WG 11. The resultant standard will be published as twin text by ITU-T and ISO/IEC; in the latter case, it will also be known as MPEG-H Part 2. This paper describes the block partitioning structure of the draft HEVC standard and presents the results of an analysis of coding efficiency and complexity. Of the many new technical aspects of HEVC, the block partitioning structure has been identified as representing one of the most significant changes relative to previous video coding standards. In contrast to the fixed size 16 × 16 macroblock structure of H.264/AVC, HEVC defines three different units according to their functionalities. The coding unit defines a region sharing the same prediction mode, e.g., intra and inter, and it is represented by the leaf node of a quadtree structure. The prediction unit defines a region sharing the same prediction information. The transform unit, specified by another quadtree, defines a region sharing the same transformation. This paper introduces technical details of the block partitioning structure of HEVC with an emphasis on the method of designing a consistent framework by combining the three different units together. Experimental results are provided to justify the role of each component of the block partitioning structure and a comparison with the H.264/AVC design is performed.

A concept of a quadtree based nonsquare block coding structure is presented in this paper for the emerging High Efficiency Video Coding standard, which includes a quadtree based asymmetric motion partitioning scheme and a nonsquare quadtree transform (NSQT) algorithm. Nonsquare motion partitioning in inter frame coding provides the possibility of getting more accurate prediction results by splitting one coding block into two nonsquare prediction blocks. Contrary to the traditional symmetric motion partitions (SMP), asymmetric motion partitions (AMP) are proposed to improve the coding efficiency, especially for the coding blocks with irregular object boundaries. NSQT is designed for nonsquare prediction blocks (SMP and AMP), which combines square and nonsquare transform blocks in a unified transform structure. It exploits the directional characteristic of an image block to improve the transform efficiency. The combination of nonsquare partitions and NSQT provides high coding flexibility and low implementation cost for both encoder and decoder design. Simulation results show that about 0.9%-2.8% bit-rate saving can be achieved in terms of different configurations, and subjective quality can also be improved.

This paper proposes a novel video compression scheme based on a highly flexible hierarchy of unit representation which includes three block concepts: coding unit (CU), prediction unit (PU), and transform unit (TU). This separation of the block structure into three different concepts allows each to be optimized according to its role; the CU is a macroblock-like unit which supports region splitting in a manner similar to a conventional quadtree, the PU supports nonsquare motion partition shapes for motion compensation, while the TU allows the transform size to be defined independently from the PU. Several other coding tools are extended to arbitrary unit size to maintain consistency with the proposed design, e.g., transform size is extended up to 64 × 64 and intraprediction is designed to support an arbitrary number of angles for variable block sizes. Other novel techniques such as a new noncascading interpolation Alter design allowing arbitrary motion accuracy and a leaky prediction technique using both open-loop and closed-loop predictors are also introduced. The video codec described in this paper was a candidate in the competitive phase of the high-efficiency video coding (HEVC) standardization work. Compared to H.264/AVC, it demonstrated bit rate reductions of around 40% based on objective measures and around 60% based on subjective testing with 1080 p sequences. It has been partially adopted into the first standardization model of the collaborative phase of the HEVC effort.

To achieve more efficient usage of channel bandwidth and provide better protection for the media payload transmitted over lossy packet-switched networks, we introduce a new scheme of generic uneven level protection (ULP) forward error correction. The scheme provides different protection levels for data of different significance within a packet. The ULP scheme is designed to be independent from the nature of the media that it protects, and it is very flexible for any protection configuration the user might need without using any out-of-band signaling. Simulation using a video stream transmitted over a lossy packet-switched network shows that the ULP algorithm achieves significant gain for the quality of the transmission over a wide range of network conditions.