arXiv subjects

Angeliki Katsenou

Publications and source records attributed to Angeliki Katsenou.

At least 19 recordsLinked to original sources

arXiv2026

FGSVQA: Frequency-Guided Short-form Video Quality Assessment

Short-form video poses new challenges to the quality assessment of user-generated content (UGC) due to its complex generation pipeline, rapid content variation, and mixed distortions. To address this challenge, we propose an end-to-end video quality assessment (VQA) framework that employs a dense visual encoder based on CLIP, and incorporates compression priors derived from the frequency domain to generate artifact- and structure-aware weight maps for feature aggregation. By explicitly decomposing artifact, structure, and original visual feature branches and adaptively fusing them over time through a learned gating module, the proposed method achieves accurate and efficient quality prediction. Experimental results show that our method achieves strong performance on short-form video datasets in terms of average rank and linear correlation (SRCC: 0.736, PLCC: 0.787), while maintaining efficient inference runtime. The code and additional results are available at: https://github.com/xinyiW915/FGSVQA.

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arXiv2026

Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming

Adaptive video streaming has facilitated improved video streaming over the past years. A balance among coding performance objectives such as bitrate, video quality, and decoding complexity is required to achieve efficient, content- and codec-dependent, adaptive video streaming. This paper proposes a multi-objective Pareto-front (PF) optimization framework to construct quality-monotonic, content-adaptive bitrate ladders Versatile Video Coding (VVC) streaming that jointly optimize video quality, bitrate, and decoding time, which is used as a practical proxy for decoding energy. Two strategies are introduced: the Joint Rate-Quality-Time Pareto Front (JRQT-PF) and the Joint Quality-Time Pareto Front (JQT-PF), each exploring different tradeoff formulations and objective prioritizations. The ladders are constructed under quality monotonicity constraints during adaptive streaming to ensure a consistent Quality of Experience (QoE). Experiments are conducted on a large-scale UHD dataset (Inter-4K), with quality assessed using PSNR, VMAF, and XPSNR, and complexity measured via decoding time and energy consumption. The JQT-PF method achieves 11.76% average bitrate savings while reducing average decoding time by 0.29% to maintain the same XPSNR, compared to a widely-used fixed ladder. More aggressive configurations yield up to 27.88% bitrate savings at the cost of increased complexity. The JRQT-PF strategy, on the other hand, offers more controlled tradeoffs, achieving 6.38 % bitrate savings and 6.17 % decoding time reduction. This framework outperforms existing methods, including fixed ladders, VMAF- and XPSNR-based dynamic resolution selection, and complexity-aware benchmarks. The results confirm that PF optimization with decoding time constraints enables sustainable, high-quality streaming tailored to network and device capabilities.

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arXiv2025

Guiding WaveMamba with Frequency Maps for Image Debanding

Compression at low bitrates in modern codecs often introduces banding artifacts, especially in smooth regions such as skies. These artifacts degrade visual quality and are common in user-generated content due to repeated transcoding. We propose a banding restoration method that employs the Wavelet State Space Model and a frequency masking map to preserve high-frequency details. Furthermore, we provide a benchmark of open-source banding restoration methods and evaluate their performance on two public banding image datasets. Experimentation on the available datasets suggests that the proposed post-processing approach effectively suppresses banding compared to the state-of-the-art method (a DBI value of 0.082 on BAND-2k) while preserving image textures. Visual inspections of the results confirm this. Code and supplementary material are available at: https://github.com/xinyiW915/Debanding-PCS2025.

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arXiv2025

Content Adaptive Encoding For Interactive Game Streaming

Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead.

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arXiv2025

CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video

The prevalence of user-generated content (UGC) on platforms such as YouTube and TikTok has rendered no-reference (NR) perceptual video quality assessment (VQA) vital for optimizing video delivery. Nonetheless, the characteristics of non-professional acquisition and the subsequent transcoding of UGC video on sharing platforms present significant challenges for NR-VQA. Although NR-VQA models attempt to infer mean opinion scores (MOS), their modeling of subjective scores for compressed content remains limited due to the absence of fine-grained perceptual annotations of artifact types. To address these challenges, we propose CAMP-VQA, a novel NR-VQA framework that exploits the semantic understanding capabilities of large vision-language models. Our approach introduces a quality-aware prompting mechanism that integrates video metadata (e.g., resolution, frame rate, bitrate) with key fragments extracted from inter-frame variations to guide the BLIP-2 pretraining approach in generating fine-grained quality captions. A unified architecture has been designed to model perceptual quality across three dimensions: semantic alignment, temporal characteristics, and spatial characteristics. These multimodal features are extracted and fused, then regressed to video quality scores. Extensive experiments on a wide variety of UGC datasets demonstrate that our model consistently outperforms existing NR-VQA methods, achieving improved accuracy without the need for costly manual fine-grained annotations. Our method achieves the best performance in terms of average rank and linear correlation (SRCC: 0.928, PLCC: 0.938) compared to state-of-the-art methods. The source code and trained models, along with a user-friendly demo, are available at: https://github.com/xinyiW915/CAMP-VQA.

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arXiv2025

DIVA-VQA: Detecting Inter-frame Variations in UGC Video Quality

The rapid growth of user-generated (video) content (UGC) has driven increased demand for research on no-reference (NR) perceptual video quality assessment (VQA). NR-VQA is a key component for large-scale video quality monitoring in social media and streaming applications where a pristine reference is not available. This paper proposes a novel NR-VQA model based on spatio-temporal fragmentation driven by inter-frame variations. By leveraging these inter-frame differences, the model progressively analyses quality-sensitive regions at multiple levels: frames, patches, and fragmented frames. It integrates frames, fragmented residuals, and fragmented frames aligned with residuals to effectively capture global and local information. The model extracts both 2D and 3D features in order to characterize these spatio-temporal variations. Experiments conducted on five UGC datasets and against state-of-the-art models ranked our proposed method among the top 2 in terms of average rank correlation (DIVA-VQA-L: 0.898 and DIVA-VQA-B: 0.886). The improved performance is offered at a low runtime complexity, with DIVA-VQA-B ranked top and DIVA-VQA-L third on average compared to the fastest existing NR-VQA method. Code and models are publicly available at: https://github.com/xinyiW915/DIVA-VQA.

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arXiv2025

ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment

With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild is increasingly evident. UGC is typically acquired using consumer devices and undergoes multiple rounds of compression (transcoding) before reaching the end user. Therefore, traditional quality metrics that employ the original content as a reference are not suitable. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the quality of diverse video content without reference to the original uncompressed videos. ReLaX-VQA uses frame differences to select spatio-temporal fragments intelligently together with different expressions of spatial features associated with the sampled frames. These are then used to better capture spatial and temporal variabilities in the quality of neighbouring frames. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features from Residual Networks and Vision Transformers. Extensive testing across four UGC datasets demonstrates that ReLaX-VQA consistently outperforms existing NR-VQA methods, achieving an average SRCC of 0.8658 and PLCC of 0.8873. Open-source code and trained models that will facilitate further research and applications of NR-VQA can be found at https://github.com/xinyiW915/ReLaX-VQA.

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arXiv2025

Connectivity for AI enabled cities -- A field survey based study of emerging economies

The impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Although AI immense potential for positively impacting urban living, its success depends on overcoming significant challenges, particularly in telecommunications infrastructure. Smart city applications, such as, federated learning, Internet of Things (IoT), and online financial services, require reliable Quality of Service (QoS) from telecommunications networks to ensure effective information transfer. However, with over three billion people underserved or lacking access to internet, many of these AI-driven applications are at risk of either remaining underutilized or failing altogether. Furthermore, many IoT and video-based applications in densely populated urban areas require high-quality connectivity. This paper explores these issues, focusing on the challenges that need to be mitigated to make AI succeed in emerging countries, where more than 80% of the world population resides and urban migration grows. In this context, an overview of a case study conducted in Kathmandu, Nepal, highlights citizens' aspirations for affordable, high-quality internet-based services. The findings underscore the pressing need for advanced telecommunication networks to meet diverse user requirements while addressing investment and infrastructure gaps. This discussion provides insights into bridging the digital divide and enabling AI's transformative potential in urban areas.

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arXiv2024

Connecting the Unconnected: A DT Case Study of Nomadic Nodes Deployment in Nepal

This paper addresses the challenge of robust cellular connectivity in dense, underdeveloped urban environments, specifically focusing on Kathmandu, Nepal. As cities grow, existing cellular infrastructure struggles to meet the demand for reliable, high-throughput, and low-latency communication services. The lack of investment in new technologies and the intricacies of the cities' landscape pose even more difficulties for robust connectivity. This work addresses the above challenges in a cost-effective and flexible way. We investigate the deployment of LTE Nomadic Nodes (NNs) at scale in order to enhance network capacity and coverage. Utilising a Digital Twin (DT), we simulate and optimise NN placement, considering Kathmandu's physical and environmental characteristics. Our approach leverages the DRIVE DT framework, which enables the systemic evaluation of various network configurations and user mobility scenarios. The results demonstrate that NNs significantly improve signal strength and expected user datarates, presenting a viable solution for enhancing urban cellular connectivity.

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arXiv2024

Decoding Complexity-Rate-Quality Pareto-Front for Adaptive VVC Streaming

Pareto-front optimization is crucial for addressing the multi-objective challenges in video streaming, enabling the identification of optimal trade-offs between conflicting goals such as bitrate, video quality, and decoding complexity. This paper explores the construction of efficient bitrate ladders for adaptive Versatile Video Coding (VVC) streaming, focusing on optimizing these trade-offs. We investigate various ladder construction methods based on Pareto-front optimization, including exhaustive Rate-Quality and fixed ladder approaches. We propose a joint decoding time-rate-quality Pareto-front, providing a comprehensive framework to balance bitrate, decoding time, and video quality in video streaming. This allows streaming services to tailor their encoding strategies to meet specific requirements, prioritizing low decoding latency, bandwidth efficiency, or a balanced approach, thus enhancing the overall user experience. The experimental results confirm and demonstrate these opportunities for navigating the decoding time-rate-quality space to support various use cases. For example, when prioritizing low decoding latency, the proposed method achieves decoding time reduction of 14.86% while providing Bjontegaard delta rate savings of 4.65% and 0.32dB improvement in the eXtended Peak Signal-to-Noise Ratio (XPSNR)-Rate domain over the traditional fixed ladder solution.

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arXiv2024

Rate-Quality or Energy-Quality Pareto Fronts for Adaptive Video Streaming?

Adaptive video streaming is a key enabler for optimising the delivery of offline encoded video content. The research focus to date has been on optimisation, based solely on rate-quality curves. This paper adds an additional dimension, the energy expenditure, and explores construction of bitrate ladders based on decoding energy-quality curves rather than the conventional rate-quality curves. Pareto fronts are extracted from the rate-quality and energy-quality spaces to select optimal points. Bitrate ladders are constructed from these points using conventional rate-based rules together with a novel quality-based approach. Evaluation on a subset of YouTube-UGC videos encoded with x.265 shows that the energy-quality ladders reduce energy requirements by 28-31% on average at the cost of slightly higher bitrates. The results indicate that optimising based on energy-quality curves rather than rate-quality curves and using quality levels to create the rungs could potentially improve energy efficiency for a comparable quality of experience.

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arXiv2023

Comparative Study of Hardware and Software Power Measurements in Video Compression

The environmental impact of video streaming services has been discussed as part of the strategies towards sustainable information and communication technologies. A first step towards that is the energy profiling and assessment of energy consumption of existing video technologies. This paper presents a comprehensive study of power measurement techniques in video compression, comparing the use of hardware and software power meters. An experimental methodology to ensure reliability of measurements is introduced. Key findings demonstrate the high correlation of hardware and software based energy measurements for two video codecs across different spatial and temporal resolutions at a lower computational overhead.

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arXiv2023

UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment

The volume of User Generated Content (UGC) has increased in recent years. The challenge with this type of content is assessing its quality. So far, the state-of-the-art metrics are not exhibiting a very high correlation with perceptual quality. In this paper, we explore state-of-the-art metrics that extract/combine natural scene statistics and deep neural network features. We experiment with these by introducing saliency maps to improve perceptibility. We train and test our models using public datasets, namely, YouTube-UGC and KoNViD-1k. Preliminary results indicate that high correlations are achieved by using only deep features while adding saliency is not always boosting the performance. Our results and code will be made publicly available to serve as a benchmark for the research community and can be found on our project page: https://github.com/xinyiW915/SPIE-2023-Supplementary.

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arXiv2023

Subjective assessment of the impact of a content adaptive optimiser for compressing 4K HDR content with AV1

Since 2015 video dimensionality has expanded to higher spatial and temporal resolutions and a wider colour gamut. This High Dynamic Range (HDR) content has gained traction in the consumer space as it delivers an enhanced quality of experience. At the same time, the complexity of codecs is growing. This has driven the development of tools for content-adaptive optimisation that achieve optimal rate-distortion performance for HDR video at 4K resolution. While improvements of just a few percentage points in BD-Rate (1-5\%) are significant for the streaming media industry, the impact on subjective quality has been less studied especially for HDR/AV1. In this paper, we conduct a subjective quality assessment (42 subjects) of 4K HDR content with a per-clip optimisation strategy. We correlate these subjective scores with existing popular objective metrics used in standard development and show that some perceptual metrics correlate surprisingly well even though they are not tuned for HDR. We find that the DSQCS protocol is too insensitive to categorically compare the methods but the data allows us to make recommendations about the use of experts vs non-experts in HDR studies, and explain the subjective impact of film grain in HDR content under compression.

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arXiv2023

Recommendations for Verifying HDR Subjective Testing Workflows

Over the past few years, there has been an increase in the demand and availability of High Dynamic Range (HDR) displays and content. To ensure the production of high-quality materials, human evaluation is required. However, ascertaining whether the full playback pipeline is indeed HDR-compliant can be challenging. In this paper, we present a set of recommendations for conformance testing to validate various aspects of the testing workflow, including playback, displays, brightness, colours, and viewing environment. We assessed the effectiveness of HDR conversion techniques used in current standards development (3GPP) for making source materials. Additionally, we evaluate HDR display technologies, including OLED and LCD, using both consumer television and a reference monitor.

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arXiv2023

Comparison of HDR quality metrics in Per-Clip Lagrangian multiplier optimisation with AV1

The complexity of modern codecs along with the increased need of delivering high-quality videos at low bitrates has reinforced the idea of a per-clip tailoring of parameters for optimised rate-distortion performance. While the objective quality metrics used for Standard Dynamic Range (SDR) videos have been well studied, the transitioning of consumer displays to support High Dynamic Range (HDR) videos, poses a new challenge to rate-distortion optimisation. In this paper, we review the popular HDR metrics DeltaE100 (DE100), PSNRL100, wPSNR, and HDR-VQM. We measure the impact of employing these metrics in per-clip direct search optimisation of the rate-distortion Lagrange multiplier in AV1. We report, on 35 HDR videos, average Bjontegaard Delta Rate (BD-Rate) gains of 4.675%, 2.226%, and 7.253% in terms of DE100, PSNRL100, and HDR-VQM. We also show that the inclusion of chroma in the quality metrics has a significant impact on optimisation, which can only be partially addressed by the use of chroma offsets.

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arXiv2022

Direct Optimisation of $\boldsymbolλ$ for HDR Content Adaptive Transcoding in AV1

Since the adoption of VP9 by Netflix in 2016, royalty-free coding standards continued to gain prominence through the activities of the AOMedia consortium. AV1, the latest open source standard, is now widely supported. In the early years after standardisation, HDR video tends to be under served in open source encoders for a variety of reasons including the relatively small amount of true HDR content being broadcast and the challenges in RD optimisation with that material. AV1 codec optimisation has been ongoing since 2020 including consideration of the computational load. In this paper, we explore the idea of direct optimisation of the Lagrangian $λ$ parameter used in the rate control of the encoders to estimate the optimal Rate-Distortion trade-off achievable for a High Dynamic Range signalled video clip. We show that by adjusting the Lagrange multiplier in the RD optimisation process on a frame-hierarchy basis, we are able to increase the Bjontegaard difference rate gains by more than 3.98$\times$ on average without visually affecting the quality.

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arXiv2022

Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs

The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that from the tested codecs and configurations, SVT-AV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user.

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