# Video Quality Assessment with Texture Information Fusion for Streaming Applications

## TL;DR

This paper introduces VQ-TIF, a fast and energy-efficient video quality assessment model that fuses texture information and features to accurately estimate perceived video quality in streaming applications.

## Contribution

The paper presents a novel DCT-energy-based VQA model with texture fusion that significantly improves speed and reduces energy consumption compared to existing methods.

## Key findings

- Achieves a PCC of 0.96 with ground truth VMAF scores.
- Estimates quality 9.14 times faster than VMAF.
- Reduces energy consumption by 89.44% at 2160p resolution.

## Abstract

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.