Search Results (14)

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

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

The VVC standard achieves high compression efficiency through its flexible QTMT partitioning structure, at the cost of significantly increased encoding complexity. In this paper, a fast QTMT partition decision method for VVC inter-coding is proposed to reduce computational complexity while preserving rate–distortion efficiency. The proposed approach exploits texture characteristics derived from GLCM analysis to guide partitioning decisions. A feature selection process identifies homogeneity as the most relevant descriptor for characterizing partitioning behavior. Based on this descriptor, a GBM model is trained to learn adaptive decision thresholds that enable a homogeneity-driven restriction of QTMT partition candidates. By progressively limiting unnecessary partition evaluations according to local texture properties, the proposed method reduces the reliance on exhaustive rate–distortion optimization through a lightweight and content-aware decision strategy. Experimental results demonstrate that the proposed approach achieves substantial encoding time reduction with negligible impact on coding performance. Full article

Quantum communication presents new opportunities for overcoming the limitations of classical wireless systems, particularly those associated with noise, fading, and interference. Building upon the principles of classical orthogonal frequency division multi-plexing (OFDM), this work proposes a quantum OFDM architecture tailored for video transmission. In the proposed system, video sequences are first compressed using the versatile video coding (VVC) standard with different group of pictures (GOP) sizes. Each GOP size is processed through a channel encoder and mapped to multi-qubit states with various qubit configurations. The quantum-encoded data is converted from serial-to-parallel form and passed through the quantum Fourier transform (QFT) to generate mutually orthogonal quantum subcarriers. Following reserialization, a cyclic prefix is appended to mitigate inter-symbol interference within the quantum channel. At the receiver, the cyclic prefix is removed, and the signal is restored to parallel before the inverse QFT (IQFT) recovers the original quantum subcarriers. Quantum decoding, classical channel decoding, and VVC reconstruction are then employed to recover the videos. Experimental evaluations across different GOP sizes and channel conditions demonstrate that quantum OFDM provides superior resilience to channel noise and improved perceptual quality compared to classical OFDM, achieving peak signal-to-noise ratio (PSNR) up to 47.60 dB, structural similarity index measure (SSIM) up to 0.9987, and video multi-method assessment fusion (VMAF) up to 96.40. Notably, the eight-qubit encoding scheme consistently achieves the highest SNR gains across all channels, underscoring the potential of quantum OFDM as a foundation for future high-quality video transmission. Full article

As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article

For the Versatile Video Coding (VVC) standard, extensive research has been conducted on in-loop filtering to improve encoding efficiency. However, most methods use only spatial characteristics without exploiting the content correlation across multiple frames or fully utilizing the inter-channel relational information. In this paper, we introduce a novel three-stage Multi-frame Multi-channel In-loop Filtering (3-MMIF) method for VVC that improves the quality of each encoded frame by harnessing the correlations between adjacent frames and channels. Firstly, we establish a comprehensive database containing pairs of encoded and original frames across various scenes. Then, we select the nearest frames in the decode buffer as the reference frames for enhancing the quality of the current frame. Subsequently, we propose a three-stage in-loop filtering method that leverages spatio-temporal and inter-channel correlations. The three-stage method is grounded in the recently developed Residual Dense Network, benefiting from its enhanced generalization ability and feature reuse mechanism. Experimental results demonstrate that our 3-MMIF method, with the encoder’s standard filter tools activated, achieves 2.78%/4.87%/5.13% Bjøntegaard delta bit-rate (BD-Rate) reductions for the Y, U, and V channels over the VVC 17.0 codec for random access configuration on the standard test set, outperforming other VVC in-loop filter methods. Full article

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04. Full article

Versatile Video Coding (VVC), the state-of-the-art video coding standard, was developed by the Joint Video Experts Team (JVET) of ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal mode decision during the encoding process. In particular, VVC adopted the bi-prediction with CU-level weight (BCW) as one of the new tools, which enhanced the coding efficiency of conventional bi-prediction by assigning different weights to the two prediction blocks in the process of inter prediction. In this study, we investigate the statistical characteristics of input features that exhibit a correlation with the BCW and define four useful types of categories to facilitate the inter prediction of VVC. With the investigated input features, a lightweight neural network with multilayer perceptron (MLP) architecture is designed to provide high accuracy and low complexity. We propose a fast BCW mode decision method with a lightweight MLP to reduce the computational complexity of the weighted multiple bi-prediction in the VVC encoder. The experimental results show that the proposed method significantly reduced the BCW encoding complexity by up to 33% with unnoticeable coding loss, compared to the VVC test model (VTM) under the random-access (RA) configuration. Full article

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively. Full article

Inter prediction is a crucial part of hybrid video coding frameworks, and it is used to eliminate redundancy in adjacent frames and improve coding performance. During inter prediction, motion estimation is used to find the reference block that is most similar to the current block, and the following motion compensation is used to shift the reference block fractionally to obtain the prediction block. The closer the reference block is to the original block, the higher the coding efficiency is. To improve the quality of reference blocks, a quality enhancement network (RBENN) that is dedicated to reference blocks is proposed. The main body of the network consists of 10 residual modules, with two convolution layers for preprocessing and feature extraction. Each residual module consists of two convolutional layers, one ReLU activation, and a shortcut. The network uses the luma reference block as input before motion compensation, and the enhanced reference block is then filtered by the default fractional interpolation. Moreover, the proposed method can be used for both conventional motion compensation and affine motion compensation. Experimental results showed that RBENN could achieve a −1.35% BD rate on average under the low-delay P (LDP) configuration compared with the latest H.266/VVC. Full article

Versatile video coding (VVC) adopts an advanced quad-tree plus multi-type tree (QTMT) coding structure to obtain higher compression efficiency, but it comes at the cost of a considerable increase in coding complexity. To effectively reduce the coding complexity of the QTMT-based coding unit (CU) partition, we propose a fast inter CU partition method based on a temporal prediction model, which includes early termination QTMT partition and early skipping multi-type tree (MT) partition. Firstly, according to the position of the current CU, we extract the optimal CU partition information of the position corresponding to the previously coded frames. We then establish a temporal prediction model based on temporal CU partition information to predict the current CU partition. Finally, to reduce the cumulative of errors of the temporal prediction model, we further extract the motion vector difference (MVD) of the CU to determine whether the QTMT partition can be terminated early. The experimental results show that the proposed method can reduce the inter coding complexity of VVC by 23.19% on average, while the Bjontegaard delta bit rate (BDBR) is only increased by 0.97% on average under the Random Access (RA) configuration. Full article

Versatile Video Coding (VVC) is the most recent video coding standard developed by Joint Video Experts Team (JVET) that can achieve a bit-rate reduction of 50% with perceptually similar quality compared to the previous method, namely High Efficiency Video Coding (HEVC). Although VVC can support the significant coding performance, it leads to the tremendous computational complexity of VVC encoder. In particular, VVC has newly adopted an affine motion estimation (AME) method to overcome the limitations of the translational motion model at the expense of higher encoding complexity. In this paper, we proposed a context-based inter mode decision method for fast affine prediction that determines whether the AME is performed or not in the process of rate-distortion (RD) optimization for optimal CU-mode decision. Experimental results showed that the proposed method significantly reduced the encoding complexity of AME up to 33% with unnoticeable coding loss compared to the VVC Test Model (VTM). Full article

To improve coding efficiency by exploiting the local inter-component redundancy between the luma and chroma components, the cross-component linear model (CCLM) is included in the versatile video coding (VVC) standard. In the CCLM mode, linear model parameters are derived from the neighboring luma and chroma samples of the current block. Furthermore, chroma samples are predicted by the reconstructed samples in the collocated luma block with the derived parameters. However, as the CCLM design in the VVC test model (VTM)-6.0 has many conditional branches in its processes to use only available neighboring samples, the CCLM implementation in parallel processing is limited. To address this implementation issue, this paper proposes including the neighboring sample generation as the first process of the CCLM, so as to simplify the succeeding CCLM processes. As unavailable neighboring samples are replaced with the adjacent available samples by the proposed CCLM, the neighboring sample availability checks can be removed. This results in simplified downsampling filter shapes for the luma sample. Therefore, the proposed CCLM can be efficiently implemented by employing parallel processing in both hardware and software implementations, owing to the removal of the neighboring sample availability checks and the simplification of the luma downsampling filters. The experimental results demonstrate that the proposed CCLM reduces the decoding runtime complexity of the CCLM mode, with negligible impact on the Bjøntegaard delta (BD)-rate. Full article

As a newly proposed video coding standard, Versatile Video Coding (VVC) has adopted some revolutionary techniques compared to High Efficiency Video Coding (HEVC). The multiple-mode affine motion compensation (MM-AMC) adopted by VVC saves approximately 15%-25% Bjøntegaard Delta Bitrate (BD-BR), with an inevitable increase of encoding time. This paper gives an overview of both the 4-parameter affine motion model and the 6-parameter affine motion model, analyzes their performances, and proposes improved algorithms according to the symmetry of iterative gradient descent for fast affine motion estimation. Finally, the proposed algorithms and symmetric MM-AMC flame of VTM-7.0 are compared. The results show that the proposed algorithms save 6.65% total encoding time on average, which saves approximately 30% encoding time of affine motion compensation. Full article