Subjective evaluation of UHD video coded using VVC with LCEVC and ML-VVC
- URL: http://arxiv.org/abs/2601.10448v1
- Date: Thu, 15 Jan 2026 14:38:52 GMT
- Title: Subjective evaluation of UHD video coded using VVC with LCEVC and ML-VVC
- Authors: Naeem Ramzan, Muhammad Tufail Khan,
- Abstract summary: This paper presents the results of a subjective quality assessment of a multilayer video coding configuration.<n>Low Complexity Enhancement Video Coding (LCEVC) is applied as an enhancement layer on top of a Versatile Video Coding (VVC) base layer.
- Score: 2.2129910930772
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents the results of a subjective quality assessment of a multilayer video coding configuration in which Low Complexity Enhancement Video Coding (LCEVC) is applied as an enhancement layer on top of a Versatile Video Coding (VVC) base layer. The evaluation follows the same test methodology and conditions previously defined for MPEG multilayer video coding assessments, with the LCEVC enhancement layer encoded using version 8.1 of the LCEVC Test Model (LTM). The test compares reconstructed UHD output generated from an HD VVC base layer with LCEVC enhancement against two reference cases: upsampled VVC base layer decoding and multilayer VVC (ML-VVC). Two operating points are considered, corresponding to enhancement layers representing approximately 10% and 50% of the total bitrate. Subjective assessment was conducted using the Degradation Category Rating (DCR) methodology with twenty five participants, across a dataset comprising fifteen SDR and HDR sequences. The reported results include Mean Opinion Scores (MOS) with associated 95% confidence intervals, enabling comparison of perceptual quality across coding approaches and operating points within the defined test scope.
Related papers
- CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment [25.10124067341784]
We introduce CP-LLM: a Context and Pixel aware Large Language Model.<n> CP-LLM features dual vision encoders designed to independently analyze perceptual quality at both high-level (video context) and low-level (pixel distortion) granularity, along with a language decoder.<n>Experiment results demonstrate that CP-LLM achieves state-of-the-art cross-dataset performance on established VQA benchmarks and superior robustness to pixel distortions.
arXiv Detail & Related papers (2025-05-21T21:13:19Z) - FineVQ: Fine-Grained User Generated Content Video Quality Assessment [57.51274708410407]
We establish the first large-scale Fine-grained Video quality assessment Database, termed FineVD, which comprises 6104 videos with fine-grained quality scores and descriptions across multiple dimensions.<n>We propose a Fine-grained Video Quality assessment model to learn the fine-grained quality of videos, with the capabilities of quality rating, quality scoring, and quality attribution.<n>Our proposed FineVQ can produce fine-grained video-quality results and achieve state-of-the-art performance on FineVD and other commonly used-VQA datasets.
arXiv Detail & Related papers (2024-12-26T14:44:47Z) - AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results [120.95863275142727]
This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunction with the Advances in Image Manipulation (AIM) workshop at ECCV 2024.
The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos encoded with 14 codecs of various compression standards.
arXiv Detail & Related papers (2024-08-21T20:32:45Z) - Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration [11.016119119250765]
This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration.
To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space.
The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested.
arXiv Detail & Related papers (2024-08-09T12:55:23Z) - ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment [35.00766551093652]
ReLaX-VQA is a novel No-Reference Video Quality Assessment (NRVQA) model.<n>It aims to address the challenges of evaluating the quality of diverse video content without reference to the original uncompressed videos.<n>It consistently outperforms existing NR-VQA methods, achieving an average S-Score of 0.8658 and PLCC of 0.8873.
arXiv Detail & Related papers (2024-07-16T08:33:55Z) - CLIPVQA:Video Quality Assessment via CLIP [56.94085651315878]
We propose an efficient CLIP-based Transformer method for the VQA problem ( CLIPVQA)
The proposed CLIPVQA achieves new state-of-the-art VQA performance and up to 37% better generalizability than existing benchmark VQA methods.
arXiv Detail & Related papers (2024-07-06T02:32:28Z) - Hierarchical B-frame Video Coding for Long Group of Pictures [42.229439873835254]
We present an end-to-end learned video for random access that combines training on long sequences of frames, rate allocation and content adaptation on inference.
Under common test conditions, it achieves results comparable to VTM in terms of YUV-PSNR BD-Rate on some classes of videos.
On average it surpasses open LD and RA end-to-end solutions in terms of VMAF and YUV BD-Rates.
arXiv Detail & Related papers (2024-06-24T11:29:52Z) - Video compression dataset and benchmark of learning-based video-quality
metrics [55.41644538483948]
We present a new benchmark for video-quality metrics that evaluates video compression.
It is based on a new dataset consisting of about 2,500 streams encoded using different standards.
Subjective scores were collected using crowdsourced pairwise comparisons.
arXiv Detail & Related papers (2022-11-22T09:22:28Z) - Efficient VVC Intra Prediction Based on Deep Feature Fusion and
Probability Estimation [57.66773945887832]
We propose to optimize Versatile Video Coding (VVC) complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation.
Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.
arXiv Detail & Related papers (2022-05-07T08:01:32Z) - Deep Learning-Based Intra Mode Derivation for Versatile Video Coding [65.96100964146062]
An intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD)
The architecture of DLIMD is developed to adapt to different quantization parameter settings and variable coding blocks including non-square ones.
The proposed method can achieve 2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V components on the platform of Versatile Video Coding (VVC) test model.
arXiv Detail & Related papers (2022-04-08T13:23:59Z) - CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial
Scalability [13.00115213941287]
We present CAESR, a learning-based coding approach for spatial scalability based on the versatile video coding (VVC) standard.
Our framework considers a low-resolution signal encoded with VVC intra-mode as a base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as an enhancement-layer (EL) model.
Our solution is competitive with the VVC full-resolution intra coding while being scalable.
arXiv Detail & Related papers (2022-02-01T13:59:43Z) - Video Compression with CNN-based Post Processing [18.145942926665164]
We propose a new CNN-based post-processing approach, which has been integrated with two state-of-the-art coding standards, VVC and AV1.
Results show consistent coding gains on all tested sequences at various spatial resolutions, with average bit rate savings of 4.0% and 5.8% against original VVC and AV1 respectively.
arXiv Detail & Related papers (2020-09-16T10:07:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.