In recent years, video streaming applications have proliferated the demand for Video Quality Assessment VQA). Reduced reference video quality assessment (RR-VQA) is a category of VQA where certain features (e.g., texture, edges) of the original video are provided for quality assessment. It is a popular research area for various applications such as social media, online games, and video streaming. This paper introduces a reduced reference Transcoding Quality Prediction Model (TQPM) to determine the visual quality score of the video possibly transcoded in multiple stages. The quality is predicted using Discrete Cosine Transform (DCT)-energy-based features of the video (i.e., the video's brightness, spatial texture information, and temporal activity) and the target bitrate representation of each transcoding stage. To do that, the problem is formulated, and a Long Short-Term Memory (LSTM)-based quality prediction model is presented. Experimental results illustrate that, on average, TQPM yields PSNR, SSIM, and VMAF predictions with an R2 score of 0.83, 0.85, and 0.87, respectively, and Mean Absolute Error (MAE) of 1.31 dB, 1.19 dB, and 3.01, respectively, for single-stage transcoding. Furthermore, an R2 score of 0.84, 0.86, and 0.91, respectively, and MAE of 1.32 dB, 1.33 dB, and 3.25, respectively, are observed for a two-stage transcoding scenario. Moreover, the average processing time of TQPM for 4s segments is 0.328s, making it a practical VQA method in online streaming applications.

翻译：近年来，视频流应用极大地促进了视频质量评估的需求。简化参考视频质量评估是一种通过提供原始视频的特定特征来进行质量评估的评估方法。该方法广泛应用于社交媒体、在线游戏和视频流等多个研究领域。本文提出了一种简化参考的转码质量预测模型，用于评估可能经过多阶段转码的视频的视觉质量分数。该模型基于视频的离散余弦变换能量特征以及每个转码阶段的目标比特率表示来预测质量。为此，我们形式化了问题，并提出了一个基于长短期记忆网络的质量预测模型。实验结果表明，在单阶段转码场景下，TQPM对PSNR、SSIM和VMAF的平均预测R²分数分别为0.83、0.85和0.87，平均绝对误差分别为1.31 dB、1.19 dB和3.01。在双阶段转码场景下，相应的R²分数为0.84、0.86和0.91，MAE为1.32 dB、1.33 dB和3.25。此外，TQPM对4秒视频段的平均处理时间为0.328秒，使其成为在线流应用中一种实用的VQA方法。