DQP-PCQA: Deep Quantization Parameters Bring New Insight to Point Cloud Quality Assessment
Bibliographic record
Abstract
With the rapid development of immersive multimedia technology, the growing demand for high-quality visual experiences has driven the emergence of point cloud quality assessment (PCQA). While current deep learning-based PCQA models have achieved breakthroughs in performance, problems such as high computational complexity and limited model generalization ability still need to be solved. In this study, focusing on compression distortion, we analyzed and verified that the compression quantization parameter (QP) can be used as a key feature for predicting perceptual quality. Based on this, a novel no-reference point cloud perceptual quality assessment metric, DQP-PCQA, is proposed. Unlike existing PCQA models that only use mean opinion score (MOS) as a supervisory label, this study proposes a multi-objective constrained optimization scheme that adds geometric quantization parameter (GQP) and texture quantization parameter (TQP) as auxiliary supervisory labels to help the model can learn robust perceptual features that take into account both subjective quality and objective distortion. We conducted comparative experiments with other advanced PCQA models on several mainstream PCQA datasets. The results show that the DQP-PCQA model achieves fast convergence speed, excellent and stable performance, low complexity and strong generalization. Further migration experiments show that after applying our proposed method to other advanced PCQA models, the performance of the improved model is further improved. Our discovery provides new insight for PCQA research. To facilitate future reproducible research, the source code will be publicly released at https://github.com/Dds46/DQP-PCQA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".