Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff

TL;DR

This paper introduces FreqSP, a CNN-based model that predicts optimal sharpening levels for videos to balance quality enhancement and bitrate, reducing over-sharpening and bandwidth costs.

Contribution

It presents a novel frequency-assisted CNN model for adaptive sharpening level prediction considering bitrate and quality tradeoffs.

Findings

FreqSP effectively predicts sharpening levels that optimize quality and bitrate.

The method reduces over-sharpening and bandwidth usage.

Experimental results demonstrate superior performance over existing approaches.

Abstract

Sharpening is a widely adopted technique to improve video quality, which can effectively emphasize textures and alleviate blurring. However, increasing the sharpening level comes with a higher video bitrate, resulting in degraded Quality of Service (QoS). Furthermore, the video quality does not necessarily improve with increasing sharpening levels, leading to issues such as over-sharpening. Clearly, it is essential to figure out how to boost video quality with a proper sharpening level while also controlling bandwidth costs effectively. This paper thus proposes a novel Frequency-assisted Sharpening level Prediction model (FreqSP). We first label each video with the sharpening level correlating to the optimal bitrate and quality tradeoff as ground truth. Then taking uncompressed source videos as inputs, the proposed FreqSP leverages intricate CNN features and high-frequency components to estimate the optimal sharpening level. Extensive experiments demonstrate the effectiveness of our method.