# Video Quality Assessment with Texture Information Fusion for Streaming Applications

arXiv: https://arxiv.org/abs/2302.14465 · HTML: https://mlsanity.com/paper/2302.14465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [IVCA: Inter-Relation-Aware Video Complexity Analyzer](https://mlsanity.com/paper/2407.00280/) (2024)

## Abstract (arXiv)

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.