arXiv subjects

Vignesh V Menon

Publications and source records attributed to Vignesh V Menon.

At least 19 recordsLinked to original sources

arXiv2026

DQ-Ladder: A Deep Reinforcement Learning-based Bitrate Ladder for Adaptive Video Streaming

Adaptive streaming of segmented video over HTTP typically relies on a predefined set of bitrate-resolution pairs, known as a bitrate ladder. However, fixed ladders often overlook variations in content and decoding complexities, leading to suboptimal trade-offs between encoding time, decoding efficiency, and video quality. This article introduces DQ-Ladder, a deep reinforcement learning (DRL)-based scheme for constructing time- and quality-aware bitrate ladders for adaptive video streaming applications. DQ-Ladder employs predicted decoding time, quality scores, and bitrate levels per segment as inputs to a Deep Q-Network (DQN) agent, guided by a weighted reward function of decoding time, video quality, and resolution smoothness. We leverage machine learning models to predict decoding time, bitrate level, and objective quality metrics (VMAF, XPSNR), eliminating the need for exhaustive encoding or quality metric computation. We evaluate DQ-Ladder using the Versatile Video Coding (VVC) toolchain (VVenC/VVdeC) on 750 video sequences across six Apple HLS-compliant resolutions and 41 quantization parameters. Experimental results against four baselines show that DQ-Ladder achieves BD-rate reductions of at least 10.3% for XPSNR compared to the HLS ladder, while reducing decoding time by 22%. DQ-Ladder shows significantly lower sensitivity to prediction errors than competing methods, remaining robust even with up to 20% noise.

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arXiv2026

Content-Driven Frame-Level Bit Prediction for Rate Control in Versatile Video Coding

Rate control allocates bits efficiently across frames to meet a target bitrate while maintaining quality. Conventional two-pass rate control (2pRC) in Versatile Video Coding (VVC) relies on analytical rate-QP models, which often fail to capture nonlinear spatial-temporal variations, causing quality instability and high complexity due to multiple trial encodes. This paper proposes a content-adaptive framework that predicts frame-level bit consumption using lightweight features from the Video Complexity Analyzer (VCA) and quantization parameters within a Random Forest regression. On ultra-high-definition sequences encoded with VVenC, the model achieves strong correlation with ground truth, yielding R2 values of 0.93, 0.88, and 0.77 for I-, P-, and B-frames, respectively. Integrated into a rate-control loop, it achieves comparable coding efficiency to 2pRC while reducing total encoding time by 33.3%. The results show that VCA-driven bit prediction provides a computationally efficient and accurate alternative to conventional rate-QP models.

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arXiv2025

Predicting Encoding Energy from Low-Pass Anchors for Green Video Streaming

Video streaming now represents the dominant share of Internet traffic, as ever-higher-resolution content is distributed across a growing range of heterogeneous devices to sustain user Quality of Experience (QoE). However, this trend raises significant concerns about energy efficiency and carbon emissions, requiring methods to provide a trade-off between energy and QoE. This paper proposes a lightweight energy prediction method that estimates the energy consumption of high-resolution video encodings using reference encodings generated at lower resolutions (so-called anchors), eliminating the need for exhaustive per-segment energy measurements, a process that is infeasible at scale. We automatically select encoding parameters, such as resolution and quantization parameter (QP), to achieve substantial energy savings while maintaining perceptual quality, as measured by the Video Multimethod Fusion Assessment (VMAF), within acceptable limits. We implement and evaluate our approach with the open-source VVenC encoder on 100 video sequences from the Inter4K dataset across multiple encoding settings. Results show that, for an average VMAF score reduction of only 1.68, which stays below the Just Noticeable Difference (JND) threshold, our method achieves 51.22% encoding energy savings and 53.54% decoding energy savings compared to a scenario with no quality degradation.

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arXiv2025

Block-Partitioning Strategies for Accelerated Multi-rate Encoding in Adaptive VVC Streaming

The demand for efficient multi-rate encoding techniques has surged with the increasing prevalence of ultra-high-definition (UHD) video content, particularly in adaptive streaming scenarios where a single video must be encoded at multiple bitrates to accommodate diverse network conditions. While Versatile Video Coding (VVC) significantly improves compression efficiency, it introduces considerable computational complexity, making multi-rate encoding a resource-intensive task. This paper examines coding unit (CU) partitioning strategies to minimize redundant computations in VVC while preserving high video quality. We propose single- and double-bound approaches, leveraging CU depth constraints from reference encodes to guide dependent encodes across multiple QPs. These methods are evaluated using VVenC with various presets, demonstrating consistent improvements in encoding efficiency. Our methods achieve up to 11.69 % reduction in encoding time with minimal bitrate overhead (<0.6 %). Comparative Pareto-front (PF) analysis highlights the superior performance of multi-rate approaches over existing configurations. These findings validate the potential of CU-guided strategies for scalable multi-rate encoding in adaptive streaming.

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arXiv2025

ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge

This paper reports IEEE International Conference on Multimedia \& Expo (ICME) 2025 Grand Challenge on Generalizable HDR and SDR Video Quality Measurement. With the rapid development of video technology, especially High Dynamic Range (HDR) and Standard Dynamic Range (SDR) contents, the need for robust and generalizable Video Quality Assessment (VQA) methods has become increasingly demanded. Existing VQA models often struggle to deliver consistent performance across varying dynamic ranges, distortion types, and diverse content. This challenge was established to benchmark and promote VQA approaches capable of jointly handling HDR and SDR content. In the final evaluation phase, five teams submitted seven models along with technical reports to the Full Reference (FR) and No Reference (NR) tracks. Among them, four methods outperformed VMAF baseline, while the top-performing model achieved state-of-the-art performance, setting a new benchmark for generalizable video quality assessment.

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arXiv2025

Learning Quality from Complexity and Structure: A Feature-Fused XGBoost Model for Video Quality Assessment

This paper presents a novel approach for reduced-reference video quality assessment (VQA), developed as part of the recent VQA Grand Challenge. Our method leverages low-level complexity and structural information from reference and test videos to predict perceptual quality scores. Specifically, we extract spatio-temporal features using Video Complexity Analyzer (VCA) and compute SSIM values from the test video to capture both texture and structural characteristics. These features are aggregated through temporal pooling, and residual features are calculated by comparing the original and distorted feature sets. The combined features are used to train an XGBoost regression model that estimates the overall video quality. The pipeline is fully automated, interpretable, and highly scalable, requiring no deep neural networks or GPU inference. Experimental results on the challenge dataset demonstrate that our proposed method achieves competitive correlation with subjective quality scores while maintaining a low computational footprint. The model's lightweight design and strong generalization performance suit real-time streaming quality monitoring and adaptive encoding scenarios.

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arXiv2024

Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA's effectiveness, yielding an average decoding energy reduction of up to 13.45%, with minimal VMAF reduction of 0.33 points at a low-quality degradation threshold, compared to the default 60 fps encoding. Even at an aggressive threshold, DECODRA achieves significant energy savings of 13.45% while only reducing VMAF by 2.11 points. In this way, DECODRA extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.

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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

Enhancing Film Grain Coding in VVC: Improving Encoding Quality and Efficiency

This paper presents an in-depth analysis of film grain handling in open-source implementations of the Versatile Video Coding (VVC) standard. We focus on two key components: the Film Grain Analysis (FGA) module implemented in VVenC and the Film Grain Synthesis (FGS) module implemented in VVdeC. We describe the methodologies used to implement these modules and discuss the generation of Supplementary Enhancement Information (SEI) parameters to signal film grain characteristics in the encoded video sequences. Additionally, we conduct subjective and objective evaluations across Full HD videos to assess the effectiveness of film grain handling. Our results demonstrate the capability of the FGA and FGS techniques to accurately analyze and synthesize film grain, thereby improving the visual quality of encoded video content. Overall, our study contributes to advancing the understanding and implementation of film grain handling techniques in VVC open-source implementations, with implications for enhancing the viewing experience in multimedia applications.

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arXiv2024

Convex-hull Estimation using XPSNR for Versatile Video Coding

As adaptive streaming becomes crucial for delivering high-quality video content across diverse network conditions, accurate metrics to assess perceptual quality are essential. This paper explores using the eXtended Peak Signal-to-Noise Ratio (XPSNR) metric as an alternative to the popular Video Multimethod Assessment Fusion (VMAF) metric for determining optimized bitrate-resolution pairs in the context of Versatile Video Coding (VVC). Our study is rooted in the observation that XPSNR shows a superior correlation with subjective quality scores for VVC-coded Ultra-High Definition (UHD) content compared to VMAF. We predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration and then determine the convex-hull online. On average, the proposed convex-hull using XPSNR (VEXUS) achieves an overall quality improvement of 5.84 dB PSNR and 0.62 dB XPSNR while maintaining the same bitrate, compared to the default UHD encoding using the VVenC encoder, accompanied by an encoding time reduction of 44.43% and a decoding time reduction of 65.46%. This shift towards XPSNR as a guiding metric shall enhance the effectiveness of adaptive streaming algorithms, ensuring an optimal balance between bitrate efficiency and perceptual fidelity with advanced video coding standards.

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arXiv2024

Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming

Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this realization, we predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration using an XGBoost-based model. Based on the predicted XPSNR scores, we introduce a Quality-A ware Dynamic Resolution Adaptation (QADRA) framework for adaptive video streaming applications, where we determine the convex-hull online. Furthermore, keeping the encoding and decoding times within an acceptable threshold is mandatory for smooth and energy-efficient streaming. Hence, QADRA determines the encoding resolution and quantization parameter (QP) for each target bitrate by maximizing XPSNR while constraining the maximum encoding and/ or decoding time below a threshold. QADRA implements a JND-based representation elimination algorithm to remove perceptually redundant representations from the bitrate ladder. QADRA is an open-source Python-based framework published under the GNU GPLv3 license. Github: https://github.com/PhoenixVideo/QADRA Online documentation: https://phoenixvideo.github.io/QADRA/

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arXiv2024

Video Super-Resolution for Optimized Bitrate and Green Online Streaming

Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.

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arXiv2024

Gain of Grain: A Film Grain Handling Toolchain for VVC-based Open Implementations

Film grain is a distinctive visual characteristic cherished by filmmakers and cinephiles for its ability to evoke nostalgia and artistic aesthetics. However, faithful preservation of film grain during encoding poses unique challenges. Film grain introduces random noise, complicating traditional compression techniques. Consequently, specialized algorithms and encoding strategies have emerged, aiming to strike a harmonious equilibrium. This paper delves into the nuanced realm of film grain handling in Versatile Video Coding (VVC) encoding. We explore the delicate balance between retaining the cinematic charm of film grain and achieving efficient compression. Moreover, we discuss the importance of perceptual quality assessment and adaptive encoding techniques in preserving film grain fidelity. Additionally, we delve into the impact of film grain handling on bitrate control and compression efficiency using VVenC, an open and optimized VVC encoder. Understanding the role of film grain and its nuanced treatment within encoders becomes increasingly pivotal for delivering high-quality, grain-inclusive content in the digital age.

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arXiv2024

Energy-efficient Adaptive Video Streaming with Latency-Aware Dynamic Resolution Encoding

Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. However, keeping the encoding time within an acceptable threshold for a smooth user experience is important to reduce the carbon footprint and energy consumption on encoding servers in video streaming applications. Toward this realization, we introduce an encoding latency-a ware dynamic resolution encoding scheme (LADRE) for adaptive video streaming applications. LADRE determines the encoding resolution for each target bitrate by utilizing a random forest-based prediction model for every video segment based on spatiotemporal features and the acceptable target latency. Experimental results show that LADRE achieves an overall average quality improvement of 0.58 dB PSNR and 0.43 dB XPSNR while maintaining the same bitrate, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 200 s segments using the VVenC encoder, when the encoding latency for each representation is set to remain below the 200 s threshold. This is accompanied by an 84.17 % reduction in overall encoding energy consumption.

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arXiv2024

Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low latency Encoding

In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70 %, a reduction in the total number of CPU threads used by 63.83 %, and a 37.87 % reduction in the overall encoding time, considering a JND of six VMAF points.

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arXiv2024

Video Quality Assessment with Texture Information Fusion for Streaming Applications

The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. We propose a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF) model for video streaming applications that determines the visual quality of the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the frames from the original and reconstructed videos and fuses them using a long short-term memory (LSTM)-based model to estimate the visual quality. Experimental results show that VQ-TIF estimates the visual quality with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy consumption, assuming an Ultra HD (2160p) display resolution.

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arXiv2023

Content-Adaptive Variable Framerate Encoding Scheme for Green Live Streaming

Adaptive live video streaming applications use a fixed predefined configuration for the bitrate ladder with constant framerate and encoding presets in a session. However, selecting optimized framerates and presets for every bitrate ladder representation can enhance perceptual quality, improve computational resource allocation, and thus, the streaming energy efficiency. In particular, low framerates for low-bitrate representations reduce compression artifacts and decrease encoding energy consumption. In addition, an optimized preset may lead to improved compression efficiency. To this light, this paper proposes a Content-adaptive Variable Framerate (CVFR) encoding scheme, which offers two modes of operation: ecological (ECO) and high-quality (HQ). CVFR-ECO optimizes for the highest encoding energy savings by predicting the optimized framerate for each representation in the bitrate ladder. CVFR-HQ takes it further by predicting each representation's optimized framerate-encoding preset pair using low-complexity discrete cosine transform energy-based spatial and temporal features for compression efficiency and sustainable storage. We demonstrate the advantage of CVFR using the x264 open-source video encoder. The results show that CVFR-ECO yields an average PSNR and VMAF increase of 0.02 dB and 2.50 points, respectively, for the same bitrate, compared to the fastest preset highest framerate encoding. CVFR-ECO also yields an average encoding and storage energy consumption reduction of 34.54% and 76.24%, considering a just noticeable difference (JND) of six VMAF points. In comparison, CVFR-HQ yields an average increase in PSNR and VMAF of 2.43 dB and 10.14 points, respectively, for the same bitrate. Finally, CVFR-HQ resulted in an average reduction in storage energy consumption of 83.18%, considering a JND of six VMAF points.

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arXiv2023

Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Streaming

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., High Efficiency Video Coding (HEVC), Alliance for Open Media Video 1 (AV1)) that lie below the predicted rate-distortion curve of the Advanced Video Coding (AVC) codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF score of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.

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