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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.12220 |
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| _version_ | 1866929545139978240 |
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| author | Hang, Xinyu Song, Shenpeng Huang, Zhimeng Jia, Chuanmin Ma, Siwei Gao, Wen |
| author_facet | Hang, Xinyu Song, Shenpeng Huang, Zhimeng Jia, Chuanmin Ma, Siwei Gao, Wen |
| contents | For decades, the Bjøntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4-5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? This paper addresses these concerns by reevaluating BD estimation. We present a novel approach employing a parameterized deep neural network to model R-D curves with high precision across various metrics, accompanied by a comprehensive R-D dataset. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Bjøntegaard Delta for Compression Efficiency Evaluation: Are We Calculating It Precisely and Reliably? Hang, Xinyu Song, Shenpeng Huang, Zhimeng Jia, Chuanmin Ma, Siwei Gao, Wen Multimedia For decades, the Bjøntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4-5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? This paper addresses these concerns by reevaluating BD estimation. We present a novel approach employing a parameterized deep neural network to model R-D curves with high precision across various metrics, accompanied by a comprehensive R-D dataset. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results. |
| title | Rethinking Bjøntegaard Delta for Compression Efficiency Evaluation: Are We Calculating It Precisely and Reliably? |
| topic | Multimedia |
| url | https://arxiv.org/abs/2410.12220 |