Search Results (94)

Also known as spherical or omnidirectional video, 360-degree video has become increasingly prevalent in autonomous vehicles (AVs), virtual reality (VR), augmented reality (AR), and immersive media applications. However, its compression poses unique challenges due to the projection from a spherical surface onto a 2D plane. Common projections like Equirectangular Projection (ERP) introduce significant geometric distortions, causing straight-line motions on the sphere to appear as curved trajectories in the 2D domain. Standard motion estimation in codecs like H.266 (Versatile Video Coding, VVC) relies on translational (linear) motion vectors, leading to poor prediction accuracy, large residual errors, and inflated bitrates for 360-degree video content. This paper proposes the implementation of Spherical Motion Vector (SMV) mapping in the pre-encoder stage. By performing motion vector calculation directly on the spherical coordinate system (θ, φ) before mapping to the 2D pixel grid (x, y), SMV enables accurate tracking of object motion across projection boundaries and warped regions. This approach minimizes residual data and improves overall compression efficiency. This paper details the mathematical foundations, integration with H.266, implementation considerations, and simulated performance gains. The proposed method builds on prior work in rotational and geodesic motion models while introducing pre-encoder spherical preprocessing for broader compatibility. Full article

Implicit neural representations (INRs) encode video frames as network weights, offering a new compression paradigm. A persistent limitation is that existing INR codecs train one model per target bitrate, so multi-rate deployment needs separate runs, separate checkpoints, and model reloading, costs that grow with the number of rate points. We propose a Generalized Slimmable Framework that replaces standard layers with width-configurable counterparts in most INR decoders, letting a single checkpoint serve multiple bitrates through nested weight tensors without topology changes. To recover the quality lost in shared-weight training, we introduce Slimmable Conditional Decoder Modulation (SCDM), which blends slimmable expert convolutions via width- and frame-conditioned gating. For encoder-based backbones, encoder output caching separates encoder computation from multi-width training. Across four INR backbones on DAVIS and Bunny, the framework cuts multi-rate storage by about $2.3$–$2.5\times$, and SCDM recovers substantial quality at all rate points while surpassing independently trained fixed-width models at narrow widths. Full article

The performance of Network-on-Chip (NoC) architectures is severely limited by the high dynamic power consumption and interconnect crosstalk with the scaling of deep sub-micron (DSM) technology. The coupling capacitance between wires dominates the self-capacitance in dense interconnects, and the coupling transitions contribute the most to the link power dissipation. In this paper, we propose a new low-power error correction coding scheme called Parity-based Multi-bit Transient Error Correction (PMTEC), which can simultaneously deal with self-transitions and coupling transitions in NoC links. The proposed method reduces the effective switching activity by employing a balanced encoding strategy, unlike existing coding techniques, which typically degrade the signal integrity or impose significant power overheads for particular data patterns. Experimental analysis demonstrates that PMTEC achieves strong link power reductions on various data profiles, including text, image, and video traffic, without the negative overheads of cutting-edge techniques like Duplicated Two-Dimensional Parities (DTDPs) and Crosstalk-Aware Transient Error Correction (CATEC). Furthermore, the codec’s parametric synthesis reveals a very small footprint, taking up only 2472.018 $\mathsf{\mu }{\mathrm{m}}^2$ of silicon area and using a negligible 0.219 $\mathsf{\mu }$W of power, which are 87.57% and 99.20% reductions over CATEC, respectively. The suggested work offers a scalable and energy-efficient solution for dependable on-chip communication in power-constrained systems, despite requiring a 15.371 ns latency trade-off to correct 8-bit bursts. Full article

3D Gaussian Splatting (3DGS) has recently emerged as an effective representation for immersive 3D scene rendering, providing high visual fidelity and real-time rendering efficiency. To support interoperable compression of trained 3DGS content, the Moving Picture Experts Group (MPEG) is exploring Gaussian Splat Coding (GSC), which mainly targets already trained 3DGS models following the INRIA reference format. The current video-based GSC anchor reorders 3DGS attributes into 2D attribute maps using Parallel Assignment Linear Sorting (PLAS) and compresses the resulting maps using High Efficiency Video Coding (HEVC). However, higher-order spherical harmonic coefficients (SH-AC) often remain irregular and exhibit low local spatial correlation even after PLAS reordering, limiting the coding efficiency of conventional video codecs. This paper proposes a VQ-HEVC hybrid compression framework that is structurally compatible with the video-based GSC anchor framework, in which SH-AC coefficients are represented by vector quantization (VQ) indices, while the remaining attributes are encoded using the same HEVC-based procedure as the GSC anchor. The proposed method adopts a two-stage VQ scheme that combines coarse VQ and product-quantization-based residual quantization, together with zero-masked residual VQ and flexible PQ grouping, to improve index-map coding efficiency across rate points. The generated VQ indices are packed into YUV400 index-map sequences and encoded using HEVC lossless coding, while the corresponding codebooks are transmitted as metadata. Experimental results on the Bartender and Cinema sequences of the MPEG GSC CTC demonstrate consistent rate–distortion improvements over the video-based GSC anchor across multiple objective quality metrics within the evaluated setting. In terms of RGB-PSNR, the proposed method achieves BD-rate reductions of 22.3% and 18.5% for the Bartender and Cinema datasets, respectively. These results suggest that, for the evaluated GSC CTC sequences, VQ-based SH-AC representation can effectively complement PLAS-based video coding while maintaining consistency with the existing GSC coding structure. Full article

The rapid growth of video content development requires more effective compression solutions than traditional ones. Although neural video compression has demonstrated impressive advances, the current methods are having a hard time with how to effectively model long-range temporal dependencies and react to different content properties. We introduce STAC (Spatio-Temporal Adaptive Context), a transformer-based neural video compression scheme that does not have these limitations, and makes three original contributions. First, the Adaptive Context Selector (ACS) is the dynamic evaluation and selection of the most informative reference frames, based on learned relevance scoring, in place of the traditional use of predetermined adjacent frame sets. Second, Enhanced Sliding Window Attention (ESWA) is an effective computational model of spatio-temporal correlations by the integration of learnable local bias and temporal gating information into a computationally adjustable attention model. Third, a dual-path entropy model is an adaptively learned fusion gate that combines channel-wise autoregressive prediction with spatio-temporal prediction to produce better probability estimations for entropy coding. Trained on the Vimeo-90k dataset using a four-phase curriculum with the Adam optimiser over approximately 2.2 M total steps. We tested STAC using six benchmark videos, such as UVG, MCL-JCV, and HEVC Class B, C, D and E videos, at varying test settings. The experimental findings prove that STAC, on average, saves a BD-rate of 32.20% in the YUV colourspace with an intra-period of −1. The consistent improvement across both PSNR and MS-SSIM metrics confirms that STAC’s coding gains arise from genuinely improved probability modelling, rather than metric-specific optimisation. Evaluations were performed on six standard benchmarks (UVG, MCL-JCV, and HEVC Classes B, C, D, and E) under 24 experimental configurations (six datasets × 2, and colourspaces × 2 intra-period settings), with all methods tested under identical conditions using the same sequences, frames (96 per sequence), and VTM-17.0 anchor codec. STAC achieves 32.20% average BD-rate savings over VTM under YUV IP = −1, outperforming the prior state-of-the-art DCMVC by 2.70 percentage points. Under IP = 32, STAC achieves −27.01%, with only 5.19 pp degradation versus 6.42 pp for DCMVC. The results generalise to the RGB colourspace (−31.23%) and scale from 240p (−35.19%) to 4K (−36.35%). Full article

Video compression is central to large-scale video delivery, where better rate–distortion efficiency directly reduces bandwidth and storage cost. A practical way to improve efficiency is to encode a low-resolution video stream with a standard codec and restore high-resolution details with a learned super-resolution model at the decoder. However, prior SR-assisted compression methods usually update the reconstruction model at fixed temporal intervals, which can waste bitrate when those update boundaries do not match actual scene changes. In this paper, we present SASVC, a scene-adaptive super-resolution video compression framework for offline codec-augmented compression. SASVC detects scene changes using frame-wise grayscale differences, updates only compact adapter modules when a content transition is observed, and compresses the resulting model updates with chained differencing, quantization, and entropy coding. In this way, the method reduces unnecessary model-stream overhead while preserving scene-specific reconstruction fidelity. Experimental results on both long-form and short-form datasets show that SASVC consistently outperforms SRVC-style baselines and conventional codec-based alternatives under the Bjontegaard delta rate based on peak signal-to-noise ratio (BD-rate/PSNR) criterion. Complementary rate–distortion (RD) comparisons in terms of structural similarity index measure (SSIM) and Video Multi-Method Assessment Fusion (VMAF) show the same overall trend, indicating that the gain is not limited to a single distortion metric. Specifically, SASVC achieves BD-rate gains of $-41.33\%$ and $-53.49\%$ on Vimeo and Xiph, respectively, and further reaches $-51.53\%$ and $-39.83\%$ on UVG and MCL-JCV. The decoder also maintains real-time 1080p reconstruction at 125 frames per second (FPS) on an NVIDIA RTX 3080 Ti GPU, indicating that scene-aligned model updates can improve compression efficiency while keeping decoder-side deployment practical. Full article

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

Reliable high-fidelity video transmission over noisy quantum channels remains challenging, especially due to temporal dependencies introduced by modern video compression standards. These codecs, such as versatile video coding (VVC), employ inter-frame prediction and group-of-pictures (GOP) structures, which are highly sensitive to channel noise and can lead to error propagation across frames. Conventional quantum encoding schemes, such as Hadamard-based superposition encoding, use fixed real-valued basis transformations that provide limited phase diversity and underutilize the multi-qubit state-space, reducing robustness under noisy quantum channels. To overcome these limitations, this study proposes a multi-qubit complex-valued orthogonal unitary superposition (COUS) encoding framework for quantum video transmission. In the proposed system, VVC-compressed video bitstreams are first protected using classical channel encoding, then segmented and mapped onto multi-qubit COUS quantum states, enabling joint amplitude and phase representation with improved resilience to quantum noise. At the receiver, transmitted quantum states undergo sequential COUS decoding, channel decoding, and VVC bitstream reconstruction to recover the original video frames. The simulation results show that COUS-based multi-qubit system outperforms the Hadamard encoding-based multi-qubit system, achieving peak signal-to-noise ratio (PSNR) up to 47.22 dB, structural similarity index measure (SSIM) up to 0.9905, and video multi-method assessment fusion (VMAF) up to 96.49. Even single-qubit COUS encoding achieves 3–4 dB channel SNR gain, while higher-qubit configurations further enhance robustness and reconstructed video quality. These results confirm that the proposed framework is scalable, noise-resilient, and provides high-fidelity quantum video transmission over noisy channels. Full article

Monitoring students’ on-screen availability is increasingly critical for analyzing participation patterns in synchronous online learning, especially under videoconferencing conditions characterized by compressed video streams, low-resolution face regions, fluctuating bandwidth, and dynamically reconfigured grid layouts. This study introduces a practical computer vision pipeline that integrates deep learning-based face detection, lightweight embedding-based identity matching, and frame-level temporal aggregation to estimate students’ visual presence (VP) during live online classes. A real-world dataset comprising 27 participants and 16,200 frames was collected under authentic conditions, including codec compression, variable image quality, and dynamic layout changes. Four widely used face detection models (Haar Cascade, DSFD, MTCNN, and YuNet) were benchmarked on noisy and low-quality images. Quantitative evaluation on a manually annotated subset of 270 frames demonstrates that MTCNN and YuNet yield lower average VP estimation errors (27.63% and 22.20%, respectively) compared to Haar Cascade (75.34%) and DSFD (47.14%), with YuNet also achieving the shortest average processing time of 0.069 s per frame. While the pipeline is intentionally streamlined to facilitate practical use by instructors, the study provides clearly defined steps and parameter settings, establishing a reproducible procedure for benchmarking face detection performance in synchronous online class environments. Full article

Multiview video coding grows exponentially with the number of views, and VVC-based systems face particularly severe computational burdens from exhaustive inter-view prediction searches. We propose VVC-MV-CM, a complexity-managed multiview extension of VVC that combines rule-based pre-screening with CNN-based adaptive inter-view prediction bypassing within a two-stage decision engine. Performance trends are observed across 19 test sequences covering planar, arc, and spherical camera configurations under all-view and selected-view encoding modes. For planar all-view configurations, VVC-MV-CM-A achieves −52.7% BD-rate relative to MIV-A with 68% encoding time reduction. Arc arrangements yield competitive performance at −1.26% (all-view) and approximately −1% (selected-view) BD-rate. Spherical configurations demonstrate −19.8% (all-view) and −15.0% (selected-view) BD-rate gains, driven by multi-reference redundancy and temporal prediction prioritization. View density analysis reveals a 4.8 percentage-point compression difference between all-view and selected-view configurations, corresponding to approximately 2.4% efficiency gain per doubling of camera count. The proposed codec achieves 1.17–1.46× encoding time relative to MIV anchors with 18–36% decoding speedup, establishing configuration-adaptive prediction as an effective and deployable approach to multiview video coding across a wide range of geometric complexities and view-sampling densities. Full article

Near-lossless coding schemes traditionally rely on uniform quantization to control the maximum absolute error ($L_{\infty }$ norm) of residual signals, often assuming a parametric model for the source distribution. This paper introduces a novel design framework for non-uniform, entropy-aware $L_{\infty }$-oriented scalar quantizers that leverages a tight and differentiable approximation of the $L_{\infty }$ distortion metric and does not require any parametric density function formulations. The framework is evaluated on both synthetic parametric sources and real-world medical depth map video datasets. For smoothly decaying distributions, such as the continuous Laplacian or discrete two-sided geometric distributions, the proposed method naturally converges to near-uniform quantizers, consistent with theoretical expectations. In contrast, for sparse or irregular sources, the algorithm produces highly non-uniform bin allocations that adapt to the local distribution structure and improve rate-distortion efficiency. When embedded in a residual-based near-lossless compression scheme, the resulting codec consistently outperforms versions equipped with uniform or piecewise-uniform quantizers, as well as state-of-the-art near-lossless schemes such as JPEG-LS and CALIC. Full article

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterprets spectrograms as visual objects and applies Feature Coding for Machines (FCM) to anomalous sound detection (ASD). In our approach, audio signals are transformed log-mel spectrograms, from which intermediate feature maps are extracted, compressed, and reconstructed through the FCM pipeline. For comparison, we implement AAC-LC (Advanced Audio Coding Low Complexity) as a representative perceptual audio codec and VVC (Versatile Video Coding) as spectrogram-based video codec. Experiments were conducted on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2023 Task 2 dataset, covering four machine types (fan, valve, toycar, slider), with anomaly detection performed using the official Autoencoder baseline model released in DCASE 2024. Detection scores were computed from reconstruction error and Mahalanobis distance. The results show that the proposed FCM-based ACoM (Audio Coding for Machines) achieves comparable or superior performance to AAC at less than half the bitrate, reliably preserving critical features even under ultra-low bitrate conditions (1.3–6.3 kbps). While VVC retains competitive performance only at high bitrates, it degrades sharply at low bitrates. These findings demonstrate that feature-based compression offers a promising direction for next-generation ACoM standardization, enabling efficient and robust ASD in bandwidth-constrained industrial environments. Full article

Recently, the demand for immersive videos has surged with the expansion of virtual reality, augmented reality, and metaverse technologies. As an international standard, moving picture experts group (MPEG) has developed MPEG immersive video (MIV) to efficiently transmit large-volume immersive videos. The MIV encoder generates atlas videos to convert extensive multi-view videos into low-bitrate formats. When these atlas videos are compressed using conventional video codecs, compression artifacts often appear in the reconstructed atlas videos. To address this issue, this study proposes a feature-extraction-based convolutional neural network (FECNN) to reduce the compression artifacts during MIV atlas video transmission. The proposed FECNN uses quantization parameter (QP) maps and depth information as inputs and consists of shallow feature extraction (SFE) blocks and deep feature extraction (DFE) blocks to utilize layered feature characteristics. Compared to the existing MIV, the proposed method improves the Bjontegaard delta bit-rate (BDBR) by −4.12% and −6.96% in the basic and additional views, respectively. Full article

Deep neural video compression codecs have shown great promise in recent years. However, there are still considerable challenges for ultra-low bitrate video coding. Inspired by recent diffusion models for image and video compression attempts, we attempt to leverage diffusion models for ultra-low bitrate portrait video compression. In this paper, we propose a predictive portrait video compression method that leverages the temporal prediction capabilities of diffusion models. Specifically, we develop a temporal diffusion predictor based on a conditional latent diffusion model, with the predicted results serving as decoded frames. We symmetrically integrate a temporal diffusion predictor at the encoding and decoding side, respectively. When the perceptual quality of the predicted results in encoding end falls below a predefined threshold, a new frame sequence is employed for prediction. While the predictor at the decoding side directly generates predicted frames as reconstruction based on the evaluation results. This symmetry ensures that the prediction frames generated at the decoding end are consistent with those at the encoding end. We also design an adaptive coding strategy that incorporates frame quality assessment and adaptive keyframe control. To ensure consistent quality of subsequent predicted frames and achieve high perceptual reconstruction, this strategy dynamically evaluates the visual quality of the predicted results during encoding, retains the predicted frames that meet the quality threshold, and adaptively adjusts the length of the keyframe sequence based on motion complexity. The experimental results demonstrate that, compared with the traditional video codecs and other popular methods, the proposed scheme provides superior compression performance at ultra-low bitrates while maintaining competitiveness in visual effects, achieving more than 24% bitrate savings compared with VVC in terms of perceptual distortion. Full article

In modern communication technology, short videos are increasingly used on social media platforms. The advancement of video codecs is pivotal in communication. In this study, we developed a new scheme to encode the residue of intraframes. For the H.264 baseline profile, we used context-based arithmetic variable-length coding (CAVLC) to encode the residue of integer transforms in a block-wise manner. In the developed method, the DC and AC coefficients are separated. In addition, context assignment, adaptive scanning, range increment, and mutual learning are adopted in a mixture of fixed-length and variable-length schemes, and block-wise compressions of the frequency table are applied to obtain improved compression rates. Compressing the frequency prevents CAVLC from being hindered by horizontally/vertically dominated blocks. The developed method outperforms CAVLC, with average reductions of 7.81, 8.58, and 7.88% in quarter common intermediate format (QCIF), common intermediate format (CIF), and full high-definition (FHD) inputs. Full article