In this paper, we present a simple but effective method to enhance blind
video quality assessment (BVQA) models for social media videos. Motivated by
previous researches that leverage pre-trained features extracted from various
computer vision models as the feature representation for BVQA, we further
explore rich quality-aware features from pre-trained blind image quality
assessment (BIQA) and BVQA models as auxiliary features to help the BVQA model
to handle complex distortions and diverse content of social media videos.
Specifically, we use SimpleVQA, a BVQA model that consists of a trainable Swin
Transformer-B and a fixed SlowFast, as our base model. The Swin Transformer-B
and SlowFast components are responsible for extracting spatial and motion
features, respectively. Then, we extract three kinds of features from Q-Align,
LIQE, and FAST-VQA to capture frame-level quality-aware features, frame-level
quality-aware along with scene-specific features, and spatiotemporal
quality-aware features, respectively. Through concatenating these features, we
employ a multi-layer perceptron (MLP) network to regress them into quality
scores. Experimental results demonstrate that the proposed model achieves the
best performance on three public social media VQA datasets. Moreover, the
proposed model won first place in the CVPR NTIRE 2024 Short-form UGC Video
Quality Assessment Challenge. The code is available at
https://github.com/sunwei925/RQ-VQA.git.

本文提出了一种简单但有效的方法来增强社交媒体视频的盲视频质量评估（BVQA）模型。通过利用来自预训练盲图像质量评估（BIQA）和 BVQA 模型的丰富质量感知特征作为辅助特征，帮助 BVQA 模型处理社交媒体视频的复杂失真和多样内容。

利用丰富的质量感知特征增强盲视频质量评估

Enhancing Blind Video Quality Assessment with Rich Quality-aware  Features

Blind video quality assessment (BVQA) plays an indispensable role in
monitoring and improving the end-users' viewing experience in various
real-world video-enabled media applications. As an experimental field, the
improvements of BVQA models have been measured primarily on a few human-rated
VQA datasets. Thus, it is crucial to gain a better understanding of existing
VQA datasets in order to properly evaluate the current progress in BVQA.
Towards this goal, we conduct a first-of-its-kind computational analysis of VQA
datasets via designing minimalistic BVQA models. By minimalistic, we restrict
our family of BVQA models to build only upon basic blocks: a video preprocessor
(for aggressive spatiotemporal downsampling), a spatial quality analyzer, an
optional temporal quality analyzer, and a quality regressor, all with the
simplest possible instantiations. By comparing the quality prediction
performance of different model variants on eight VQA datasets with realistic
distortions, we find that nearly all datasets suffer from the easy dataset
problem of varying severity, some of which even admit blind image quality
assessment (BIQA) solutions. We additionally justify our claims by contrasting
our model generalizability on these VQA datasets, and by ablating a dizzying
set of BVQA design choices related to the basic building blocks. Our results
cast doubt on the current progress in BVQA, and meanwhile shed light on good
practices of constructing next-generation VQA datasets and models.

通过设计简约的 BVQA 模型，我们对 VQA 数据集进行计算分析，发现几乎所有数据集都存在易的数据集问题，并对基本构建模块的选择进行研究，对当前 BVQA 进展表示疑虑，并为构建下一代 VQA 数据集和模型提供了指导。