Video traffic constitutes a significant share of global web traffic. To reduce its volume, video codecs have been developed and continuously improved. While the industry has achieved substantial progress in traditional video coding, neural video codecs (NVCs) have recently emerged as a new approach that applies deep learning to video compression. This creates new challenges for compression quality assessment, which is essential for the further development and improvement of such codecs. In particular, it is important to evaluate the novel temporal compression paradigms introduced by NVCs. In this work, we present a large-scale subjective dataset of videos compressed with both neural and traditional video codecs. The subjective scores were collected through crowd-sourced pairwise comparisons. The proposed dataset provides a valuable resource for the development and benchmarking of video quality metrics tailored to neural video codecs. The dataset is available at the following link: https://videoprocessing.github.io/nvc-dataset-benchmark
The proposed S-NVRC spans a wide range of bitrate and decoding-complexity using a single encoding (training) and outperforms SHM 12.4 and the multi-layer VTM-20.0, by 43.7% and 5.6% in BD-rate on the UVG dataset, while also providing flexible complexity scalability.
Tian-Hao Peng, Ho-Man Kwan, Fan Zhang et al.· 0 citations
Video semantic fidelity (SF), a metric that quantifies how well a compressed video preserves the semantic content of its source, is introduced and shows that SF correlates more strongly with human judgments than conventional video metrics.
Neural video codecs derive motion and temporal contexts mainly from RGB frames, leaving room for cross-modal guidance from complementary temporal observations. Event streams can provide such observations by recording brightness changes between frames. In this work, we propose an Event-guided Neural Video Codec (ENVC) t...
Ji-Yun Kong, Jungwoo Kim, Enes Eray Demirtas et al.· 0 citations
Instrumental quality models for video quality prediction are usually trained for a fixed set of codecs or other input features, and every new input variable requires retraining. Novel, general-purpose decision models can answer questions without task-specific training, but it is unclear whether they can judge video qua...
Learned image codecs (LICs) have demonstrated strong rate-distortion performance in academic benchmarks, but their readiness for production deployment remains unclear. In this paper, we present a comprehensive benchmarking study comparing several state-of-the-art learned image codecs — including NNVC, JPEG AI, and the...
Z. Lei, Jin-Miao Huang, Armen Abnousi et al.· Applications of Digital Imag...· 0 citations
Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to claim improvements. The reported benchmark scores in these works, however, are almost always produced by frameworks (e.g., lmms eval) used as black boxes: certain eval...
Soumya Jahagirdar, A. Kukleva, Vishaal Udandarao et al.· 0 citations
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