  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.
