  The rise in video streaming applications has increased the demand for video
quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method
Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates
with perceptual quality, but its computation is time-intensive. We propose a
Discrete Cosine Transform (DCT)-energy-based VQA with texture information
fusion (VQ-TIF) model for video streaming applications that determines the
visual quality of the reconstructed video compared to the original video.
VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the
frames from the original and reconstructed videos and fuses them using a long
short-term memory (LSTM)-based model to estimate the visual quality.
Experimental results show that VQ-TIF estimates the visual quality with a
Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE)
of 2.71, on average, compared to the ground truth VMAF scores. Additionally,
VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the
state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy
consumption, assuming an Ultra HD (2160p) display resolution.
