title: Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with
    Multimodal Large Language Model
summary: The rapid expansion of mobile internet has resulted in a substantial increase
  in user-generated content (UGC) images, thereby making the thorough assessment
  of UGC images both urgent and essential. Recently, multimodal large language
  models (MLLMs) have shown great potential in image quality assessment (IQA) and
  image aesthetic assessment (IAA). Despite this progress, effectively scoring
  the quality and aesthetics of UGC images still faces two main challenges: 1) A
  single score is inadequate to capture the hierarchical human perception. 2) How
  to use MLLMs to output numerical scores, such as mean opinion scores (MOS),
  remains an open question. To address these challenges, we introduce a novel
  dataset, named Realistic image Quality and Aesthetic (RealQA), including 14,715
  UGC images, each of which is annoted with 10 fine-grained attributes. These
  attributes span three levels: low level (e.g., image clarity), middle level
  (e.g., subject integrity) and high level (e.g., composition). Besides, we
  conduct a series of in-depth and comprehensive investigations into how to
  effectively predict numerical scores using MLLMs. Surprisingly, by predicting
  just two extra significant digits, the next token paradigm can achieve SOTA
  performance. Furthermore, with the help of chain of thought (CoT) combined with
  the learnt fine-grained attributes, the proposed method can outperform SOTA
  methods on five public datasets for IQA and IAA with superior interpretability
  and show strong zero-shot generalization for video quality assessment (VQA).
  The code and dataset will be released.
paper_authors._id: 67cf981ec8f2a661dc93a6fd
paper_authors.name: Mingxing Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:23:00.614Z
paper_authors._id: 6513a0f14f1682e4407758a9
paper_authors.avatarUrl: /avatars/b2a6886114492944cfa235363817565f.svg
paper_authors.fullname: Mingxing Li
paper_authors.type: user
paper_authors.user: MingxingLi
paper_authors._id: 67cf981ec8f2a661dc93a6fe
paper_authors.name: Rui Wang
paper_authors._id: 67cf981ec8f2a661dc93a6ff
paper_authors.name: Lei Sun
paper_authors._id: 67cf981ec8f2a661dc93a700
paper_authors.name: Yancheng Bai
paper_authors._id: 67cf981ec8f2a661dc93a701
paper_authors.name: Xiangxiang Chu
paper_title: Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with
    Multimodal Large Language Model
paper_summary: The rapid expansion of mobile internet has resulted in a substantial increase
  in user-generated content (UGC) images, thereby making the thorough assessment
  of UGC images both urgent and essential. Recently, multimodal large language
  models (MLLMs) have shown great potential in image quality assessment (IQA) and
  image aesthetic assessment (IAA). Despite this progress, effectively scoring
  the quality and aesthetics of UGC images still faces two main challenges: 1) A
  single score is inadequate to capture the hierarchical human perception. 2) How
  to use MLLMs to output numerical scores, such as mean opinion scores (MOS),
  remains an open question. To address these challenges, we introduce a novel
  dataset, named Realistic image Quality and Aesthetic (RealQA), including 14,715
  UGC images, each of which is annoted with 10 fine-grained attributes. These
  attributes span three levels: low level (e.g., image clarity), middle level
  (e.g., subject integrity) and high level (e.g., composition). Besides, we
  conduct a series of in-depth and comprehensive investigations into how to
  effectively predict numerical scores using MLLMs. Surprisingly, by predicting
  just two extra significant digits, the next token paradigm can achieve SOTA
  performance. Furthermore, with the help of chain of thought (CoT) combined with
  the learnt fine-grained attributes, the proposed method can outperform SOTA
  methods on five public datasets for IQA and IAA with superior interpretability
  and show strong zero-shot generalization for video quality assessment (VQA).
  The code and dataset will be released.
paper_ai_summary: The introduction of RealQA dataset and the use of MLLMs with chain of thought for predicting image quality and aesthetic scores achieves state-of-the-art performance and zero-shot generalization for video quality assessment.
paper_ai_keywords: multimodal large language models image quality assessment image aesthetic assessment next token paradigm chain of thought video quality assessment