Compressed Feature Quality Assessment: Dataset and Baselines

• URL: http://arxiv.org/abs/2506.07412v2
• Date: Tue, 02 Sep 2025 08:58:40 GMT
• Title: Compressed Feature Quality Assessment: Dataset and Baselines
• Authors: Changsheng Gao, Wei Zhou, Guosheng Lin, Weisi Lin,
• Abstract summary: We propose the first benchmark dataset for evaluating semantic fidelity of compressed features.<n>We systematically assess three widely used metrics -- MSE, cosine similarity, and Centered Kernel Alignment (CKA) -- in terms of their ability to capture semantic degradation.<n>This work advances the field by establishing a foundational benchmark and providing a critical resource for the community to explore CFQA.
• Score: 89.62929964441962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread deployment of large models in resource-constrained environments has underscored the need for efficient transmission of intermediate feature representations. In this context, feature coding, which compresses features into compact bitstreams, becomes a critical component for scenarios involving feature transmission, storage, and reuse. However, this compression process inevitably introduces semantic degradation that is difficult to quantify with traditional metrics. To address this, we formalize the research problem of Compressed Feature Quality Assessment (CFQA), aiming to evaluate the semantic fidelity of compressed features. To advance CFQA research, we propose the first benchmark dataset, comprising 300 original features and 12000 compressed features derived from three vision tasks and four feature codecs. Task-specific performance degradation is provided as true semantic distortion for evaluating CFQA metrics. We systematically assess three widely used metrics -- MSE, cosine similarity, and Centered Kernel Alignment (CKA) -- in terms of their ability to capture semantic degradation. Our findings demonstrate the representativeness of the proposed dataset while underscoring the need for more sophisticated metrics capable of measuring semantic distortion in compressed features. This work advances the field by establishing a foundational benchmark and providing a critical resource for the community to explore CFQA. To foster further research, we release the dataset and all associated source code at https://github.com/chansongoal/Compressed-Feature-Quality-Assessment.

Related papers

• Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation [49.48204107529758]
  We define token overflow as a regime in which compressed representations no longer contain sufficient information to answer a given query.<n>In this paper, we find that query-agnostic saturation statistics reliably separate compressed from uncompressed token representations.<n>Lightweight probing classifiers over both query and context xRAG representations detect overflow with 0.72 AUC-ROC on average.<n>These results advance from query-independent diagnostics to query-aware detectors, enabling low-cost pre-LLM gating to mitigate compression-induced errors.
  arXiv  Detail & Related papers  (2026-02-12T18:15:08Z)
• CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video [9.172799792564009]
  We propose CAMP-VQA, a novel NR-VQA framework that exploits the semantic understanding capabilities of large models.<n>Our approach introduces a quality-aware video metadata mechanism that integrates key fragments extracted from inter-frame variations.<n>Our model consistently outperforms existing NR-VQA methods, achieving improved accuracy without the need for costly manual fine-grained annotations.
  arXiv  Detail & Related papers  (2025-11-10T16:37:47Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Compressed Video Quality Enhancement: Classifying and Benchmarking over Standards [7.991926266678548]
  This paper introduces a novel taxonomy classifying CVQE methods across architectural paradigms, coding standards, and compressed-domain feature utilization.<n>Second, it proposes a unified benchmarking framework integrating modern compression protocols and standard test sequences for fair multi-criteria evaluation.<n>Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-12T16:58:20Z)
• Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features [5.409704301731714]
  PST-PCQA is a no-reference point cloud quality metric based on a low-complexity, learning-based framework.<n>It evaluates point cloud quality by analyzing individual patches, integrating local and global features to predict the Mean Opinion Score.
  arXiv  Detail & Related papers  (2025-03-19T08:52:04Z)
• RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation [58.04300937361664]
  One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the RB can be optimized for perceptual quality.<n>We propose a novel assessment method, utilizing radial basis function (RBF) to convert discrete point features into a continuous feature function for the distorted point cloud.
  arXiv  Detail & Related papers  (2025-03-18T11:25:55Z)
• SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization [29.93981107658258]
  This paper proposes a textbfSoft label compression-centric dataset condensation framework.<n>It balances informativeness, discriminativeness, and compressibility of the condensed data.<n>Experiments on large-scale datasets, including ImageNet-1K and Tiny-ImageNet, demonstrate that SCORE outperforms existing methods in most cases.
  arXiv  Detail & Related papers  (2025-03-18T06:04:44Z)
• Hierarchical Semantic Compression for Consistent Image Semantic Restoration [62.97519327310638]
  We propose a novel hierarchical semantic compression (HSC) framework that purely operates within intrinsic semantic spaces from generative models.<n> Experimental results demonstrate that the proposed HSC framework achieves the state-of-the-art performance on subjective quality and consistency for human vision.
  arXiv  Detail & Related papers  (2025-02-24T03:20:44Z)
• Disentangling CLIP for Multi-Object Perception [58.73850193789384]
  Vision-language models like CLIP excel at recognizing the single, prominent object in a scene, but struggle in complex scenes containing multiple objects.<n>We propose DCLIP, a framework that disentangles CLIP features using two complementary objectives.<n>Our experiment demonstrates that DCLIP reduces inter-class feature similarity by 30% compared to CLIP.
  arXiv  Detail & Related papers  (2025-02-05T08:20:31Z)
• HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression [25.820461699307042]
  We introduce a novel Hybrid Entropy Model for 3D Gaussian Splatting (HEMGS) to achieve hybrid lossy-lossless compression.<n>It consists of three main components: a variable-rate predictor, a hyperprior network, and an autoregressive network.<n>HEMGS achieves about a 40% average reduction in size while maintaining rendering quality over baseline methods.
  arXiv  Detail & Related papers  (2024-11-27T16:08:59Z)
• No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer [6.095342999639137]
  We develop a dual-measurement framework that combines vision Transformer (ViT)-based global feature extractor and convolutional neural networks (CNNs)-based local feature extractor.<n>We introduce a semantic-aligned quality transfer method that extends the training data by automatically labeling the quality scores of diverse image content with subjective opinion scores.
  arXiv  Detail & Related papers  (2024-08-07T16:34:32Z)
• Compression-Realized Deep Structural Network for Video Quality Enhancement [78.13020206633524]
  This paper focuses on the task of quality enhancement for compressed videos. Most of the existing methods lack a structured design to optimally leverage the priors within compression codecs. A new paradigm is urgently needed for a more conscious'' process of quality enhancement.
  arXiv  Detail & Related papers  (2024-05-10T09:18:17Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment [60.2709006613171]
  We propose simple baselines for projection-based point cloud quality assessment (PCQA) We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks.
  arXiv  Detail & Related papers  (2023-10-26T04:42:57Z)
• Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension [13.191437539419681]
  We propose a novel approach called Adaptive Bidirectional Attention-Capsule Network (ABA-Net) ABA-Net adaptively exploits the source representations of different levels to the predictor. We set the new state-of-the-art performance on the SQuAD 1.0 dataset.
  arXiv  Detail & Related papers  (2022-08-18T10:14:32Z)
• Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary [65.37544133256499]
  We propose a metric to evaluate the content quality of a summary using question-answering (QA) We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval.
  arXiv  Detail & Related papers  (2020-10-01T15:33:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.