Comprehensive Video Quality Evaluation Practices at Bilibili: From Subjective and Objective Metrics to HDR Assessment

Bilibili’s comprehensive video‑quality framework merges ITU‑R BT.500 subjective MOS testing with objective metrics such as PSNR, SSIM, VMAF and NIQE—including HDR10 and HLG assessment—through a full capture‑to‑encoding workflow, addressing alignment and content‑specific challenges and delivering measurable QoE and speed gains across its platforms.

HDRPSNRQoE

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Comprehensive Video Quality Evaluation Practices at Bilibili: From Subjective and Objective Metrics to HDR Assessment

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Jun 7, 2022 · Artificial Intelligence

How to Implement SRCNN for Image Super‑Resolution in PyTorch

This article walks through a complete PyTorch implementation of the SRCNN model for image super‑resolution, covering dataset preparation, patch extraction, model architecture, training on a GTX 770 GPU for 2500 epochs, PSNR evaluation, and visual comparisons with bicubic up‑sampling.

PSNRPatchifyPyTorch

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How to Implement SRCNN for Image Super‑Resolution in PyTorch

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May 31, 2022 · Artificial Intelligence

How Deep Convolutional Networks Boost Image Super-Resolution: A Paper Review

This article reviews the seminal SRCNN paper, detailing its contributions, architecture, training pipeline, hyper‑parameters, and extensive experiments that show how a shallow fully‑convolutional network achieves superior PSNR and runtime compared to traditional sparse‑coding and bicubic methods.

CNNPSNRSRCNN

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How Deep Convolutional Networks Boost Image Super-Resolution: A Paper Review