Livestreaming has become increasingly prevalent in modern visual communication, where automatic camera quality tuning is essential for delivering superior user Quality of Experience (QoE). Such tuning requires accurate blind image quality assessment (BIQA) to guide parameter optimization decisions. Unfortunately, the existing BIQA models typically only predict an overall coarse-grained quality score, which cannot provide fine-grained perceptual guidance for precise camera parameter tuning. To bridge this gap, we first establish FGLive-10K, a comprehensive fine-grained BIQA database containing 10,185 high-resolution images captured under varying camera parameter configurations across diverse livestreaming scenarios. The dataset features 50,925 multi-attribute quality annotations and 19,234 fine-grained pairwise preference annotations. Based on FGLive-10K, we further develop TuningIQA, a fine-grained BIQA metric for livestreaming camera tuning, which integrates human-aware feature extraction and graph-based camera parameter fusion. Extensive experiments and comparisons demonstrate that TuningIQA significantly outperforms state-of-the-art BIQA methods in both score regression and fine-grained quality ranking, achieving superior performance when deployed for livestreaming camera tuning.

The inherent diversity of visual scenes poses a fundamental challenge in blind image quality assessment (BIQA), which has become a major obstacle to the model generalization. In this study, we found that human annotations for images with different visual scenes exhibit distinct quality distribution discrepancies. The existing BIQA models tend to overfit to such diversified distributions, which in turn leads to compromised model generalizability, especially when dealing with unseen scenes in the real-world scenario. Motivated by the above facts, this paper presents a generalizable BIQA model by learning Scene-INvariant Distribution, named SIND. Specifically, we propose a distribution alignment framework to alleviate the distribution discrepancy for quality regression models, which is achieved by automatically scaling and shifting the cross-scene distributions into a unified distribution. Then, the aligned unified distribution is leveraged to supervise the model training, achieving scene-invariant and quality-aware feature representation. In addition, a token-complementary patch reasoning network is designed to extract comprehensive quality-aware features from both the image overview and detail, achieving more accurate quality prediction. Extensive experiments for both image technical- and aesthetic-quality assessment tasks show the superiority of the proposed SIND model over the state-of-the-arts. Moreover, the proposed framework is model-agnostic and can enhance model generalizability without incurring extra inference costs. The proposed method won the championship in the NTIRE 2024 Portrait Quality Assessment Challenge. Codes will be available at https://github.com/ZachL1/SIND.

The highly abstract nature of image aesthetics perception (IAP) poses a significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in MLLMs falling short of aesthetics perception capabilities. To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models. Specifically, to align MLLMs with human aesthetics perception, we construct a corpus-rich aesthetic critique database with 21,904 diverse-sourced images and 88K human natural language feedbacks, which are collected via progressive questions, ranging from coarse-grained aesthetic grades to fine-grained aesthetic descriptions. To ensure that MLLMs can handle diverse queries, we further prompt GPT to refine the aesthetic critiques and assemble the large-scale aesthetic instruction tuning dataset, i.e. AesMMIT, which consists of 409K multi-typed instructions to activate stronger aesthetic capabilities. Based on the AesMMIT database, we fine-tune the open-sourced general foundation models, achieving multi-modality Aesthetic Expert models, dubbed AesExpert. Extensive experiments demonstrate that the proposed AesExpert models deliver significantly better aesthetic perception performances than the state-of-the-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision. Project Page: https://yipoh.github.io/aes-expert/.