Source-linked AI summary
SCOPE: Semantic COdec Pre-Editing for Object-Preserving Compression
Silvia Dani, Leonardo Galteri, Marco Bertini
TL;DR
Standard codecs prioritize human perceptual quality, while machine consumption makes bitrate and detection accuracy the relevant trade-off. SCOPE conditions one pre-editor on codec family and severity, and reports gains across evaluated codecs and detectors alongside zero-shot transfer to unseen codecs.
Problem
Standard codecs target human perceptual quality, while machine-oriented compression must balance bitrate with downstream detection accuracy; existing pre-processors may be tied to a codec, quality setting, or task configuration.
Source: §1 · pp. 1–2Method
SCOPE conditions a single pre-editor on codec family and compression severity, preserving object-relevant regions while simplifying background content before compression.
Source: §1 · p. 2Results
−15.98% average BDBR relative to direct compression across JPEG, BPG, and BMSHJ and three evaluated detector architectures; SCOPE also transfers zero-shot to Cheng and JPEG2000.
Source: §5 · p. 14Takeaways & Limitations
Unseen-family performance can depend on the selected seen-family condition, as shown by the JPEG2000 transfer results.
Source: §5 · p. 14The empirical conclusions are specific to object detection, although the formulation could use other frozen downstream task models.
Source: §3.4 · p. 10
Abstract
from arXiv · showhide
Image compression is increasingly used before machine vision systems, yet standard codecs are mainly optimized for human perception rather than downstream task accuracy. We propose SCOPE (Semantic COdec Pre-Editing), a conditional image pre-editor that modifies images before compression to improve the bitrate and detection accuracy trade-off. The model is conditioned on the codec family and compression severity, allowing a single trained network to adapt to multiple compression settings without retraining or loading codec-specific weights. Instead of uniformly preserving the whole image, the pre-editor learns to protect task-relevant regions while simplifying less important background content to improve compressibility. The resulting rate-accuracy curves show consistent gains across different codec families and detector architectures, together with strong transferability to unseen learned and wavelet-based compression families.
1 Introduction
Machine-oriented compression must balance bitrate with detection accuracy, but existing task-aware approaches can require changes to codecs, transmitted representations, or downstream pipelines. SCOPE uses one codec- and severity-conditioned pre-editor to preserve object-relevant content and simplify background, with reported gains across codecs and detector architectures and transfer to unseen compression families.
1 Introduction
- Machine-oriented compression must trade bitrate against downstream task accuracy because perceptual distortion does not reliably track machine-task performance.Strong compression can damage object cues while spending bits on less relevant background content.Source: §1 · p. 1
- Earlier task-aware approaches can require changes to the codec, transmitted representation, or downstream pipeline, limiting compatibility with existing systems.Pre-processing retains a standard compressed image, but existing methods may be tied to a codec, quality setting, or task configuration.Source: §1 · p. 2
- A single SCOPE network adapts to codec family and normalized compression severity without retraining or codec-specific weights.Its attention map and object and background residuals preserve object regions while simplifying background content.Source: §1 · p. 2
- SCOPE trains with differentiable codec proxies and frozen detector supervision, without modifying the downstream detector.The paper introduces a conditional pre-editing framework with attention-based object and background editing.Source: §1 · p. 2
- SCOPE improves rate-accuracy performance across multiple codecs and detector architectures and transfers to unseen learned and wavelet-based compression families.The model is conditioned on codec family and compression severity.Source: §1 · p. 2
2 Related Work
Related work frames machine compression as a rate-accuracy problem and explores codec redesign, feature transmission, and standard-compatible pre-processing. SCOPE's distinction is to condition a pixel-space pre-editor across codec families and severity levels, using differentiable compression proxies for training.
2 Related Work
- Coding-for-machines methods optimize compression for downstream tasks, while learned codec designs can require replacing standard codecs and retraining for new settings.The rate-accuracy objective follows from the three-way trade-off among classification accuracy, signal distortion, and perceptual quality, and task-relevant distortion formulations.Source: §2 · p. 3
- Feature-compression approaches reduce bitrate by transmitting neural representations, but abandoning standard decodable images limits compatibility with existing viewers and image-based tools.Some standard-compatible wrappers instead add neural modules around codecs, though they often operate inside or after the codec.Source: §2 · pp. 3–4
- Codec-side saliency and ROI methods allocate bits by object or semantic importance, whereas SCOPE applies that object-background asymmetry in pixel space before an unmodified codec.SCOPE's attention map blends an object-preserving branch with a blurred-background branch.Source: §2 · p. 4
Preprocessing before standard codecs
- Unlike existing preprocessors that may target fixed codecs, quality settings, or tasks, SCOPE uses one set of weights across codec families and operating points.Its codec-aware conditioning addresses both family and compression severity.Source: Preprocessing before standard codecs · pp. 4–5
Learned compression backbones and conditioning
- Differentiable codec stand-ins make end-to-end training of pre-codec modules possible by allowing detector gradients to flow back to the pre-editor.The paper builds on differentiable-JPEG and learned-codec proxy formulations [3].Source: Learned compression backbones and… · p. 5
3 Proposed Method
SCOPE pre-edits images before compression to preserve task-relevant content while making non-critical regions easier to compress. It conditions one pre-editor on codec family and compression severity, and trains it with codec proxies and detector-based supervision.
3 Proposed Method
- SCOPE takes an image and codec condition (family and severity) and produces an edited image for compression.The same model is intended to adapt across compression settings without retraining or codec-specific weights.Source: §3 · p. 5
3.1 Model Architecture
- Codec family and severity condition a U-Net-style editor that blends an object-preserving residual branch with a blurred-background branch using predicted attention.The severity basis includes smooth cues and high-severity indicators for potentially nonlinear compression effects.Source: §3.1 · pp. 5–7
3.2 Training Pipeline
- Training uses box-derived masks, differentiable codec proxies, and a frozen detector to compare clean-image teacher features with features from compressed edited images.The codec proxy remains fixed, so gradients through it update only the pre-editor; training samples codec families including Diff-JPEG, BMSHJ, and a BPG-like proxy.Source: §3.2 · pp. 7–8
3.3 Training Objective
- The objective combines background simplification and rate regularization, attention supervision, detector-feature consistency, and bounded, spatially smooth residuals.Background losses encourage compressibility while avoiding disruptive artifacts; detector consistency compares teacher and student features within the detector mask.Source: §3.3 · pp. 8–9
3.4 Inference
- At inference, the pre-editor needs only an image and codec condition; boxes, masks, and detector features are not required.The edited image is then encoded by the target codec.Source: §3.4 · pp. 9–10
4 Experiments
Across trained codecs, SCOPE generally improves the mAP–bitrate trade-off, with bitrate savings across all nine codec–detector pairs. The experiments also assess qualitative behavior, zero-shot transfer, and the effects of training objectives and conditioning.
4 Experiments
- Evaluation uses 5,000 COCO 2017 validation images, codec-normalized severity, and bitrate measured from actual encoded outputs.The setup tests JPEG, BPG, and BMSHJ, and reports 11.81 ms total pre-editing time per image, or 84.7 FPS including tensor preparation and conditioning.Source: §4 · p. 10
4.1 Results
- BMSHJ and BPG gains are consistent across detectors, while JPEG gains vary by detector and concentrate at low bitrate for RT-DETR-l.For JPEG, the reported BDBR gains are −15.88% for Faster R-CNN, −14.92% for YOLO26x, and −5.71% for RT-DETR-l; the latter curves converge as compression weakens.Source: §4.1 · p. 10
- −15.98% average BDBR across nine JPEG/BPG/BMSHJ–detector pairs, with all nine negative; Fig. 3 plots rate–accuracy curves for the trained codecs and three detectors.BDBR is computed over each pair’s common AP interval without extrapolation; negative values denote bitrate savings at equal accuracy.Source: §4.1 · p. 11, Figure 3 · p. 11
4.2 Qualitative Comparison
- BMSHJ examples show SCOPE trading bitrate against detections: it may preserve baseline detections or suppress false positives, and sometimes recover missed objects at a slight bitrate increase.The examples illustrate background modification for compressibility while keeping object regions informative for detection.Source: §4.2 · p. 11, Figure 4 · p. 12
4.3 Zero-Shot Codec Transferability
- On unseen Cheng, SCOPE improves direct compression for all three detectors and reaches −20.66% BDBR for Faster R-CNN, versus −31.32% for Cheng-trained TSW-pre.The codec-specific TSW-pre result is stronger on the codec used for its training, while SCOPE transfers without Cheng-specific optimization.Source: §4.3 · p. 11, Figure 5 · p. 12
- For unseen JPEG2000, mean BDBR is −1.35%, −9.97%, and −11.92% under Diff_JPEG, BMSHJ, and BPG_like; BPG_like is strongest for all detectors.The same pretrained model is evaluated under all three seen-family conditions without target-codec validation to select a condition.Source: §4.3 · pp. 11–12, Figure 5 · p. 12, Table 1 · p. 13
4.4 Ablation Study
- Removing task loss can lower bitrate but substantially harms detection, often falling below direct compression; removing bg/rate retains task protection but makes results less stable.The task-aware and background/rate objective groups therefore serve complementary roles: protecting task evidence and reducing unnecessary bits in non-object regions.Source: §4.4 · p. 13, Figure 6 · p. 13
- Family-only reaches −15.42% BDBR versus severity-only at −13.28%; full SCOPE’s −15.98% is best overall, although codec-specific specialists can be stronger locally.The family condition is the stronger global conditioning signal, while adding severity yields the best aggregate result; the shared model retains one set of weights.Source: §4.4 · p. 14, Table 3 · p. 14
5 Conclusion
SCOPE combines codec-family and compression-severity conditioning with object/background editing to adapt one model across compression regimes. Across three codecs and three detector architectures, it improves bitrate–accuracy trade-offs and transfers zero-shot to unseen codecs.
5 Conclusion
- Codec-family and severity conditioning with attention-based object/background editing lets one set of weights adapt across compression regimes.Training uses differentiable codec proxies and a frozen detector; inference leaves the actual codec and downstream detector unchanged.Source: §5 · p. 14
- SCOPE achieves an average BDBR of −15.98% relative to direct compression across JPEG, BPG, BMSHJ, and three detector architectures.The conditioning ablation finds family information is the strongest individual signal, while combining family and severity gives the best aggregate result with one model.Source: §5 · p. 14
- SCOPE transfers zero-shot to unseen Cheng and JPEG2000 codecs, though the selected seen-family condition can affect JPEG2000 performance.Source: §5 · p. 14
- The results support conditional pre-editing as a standard-compatible strategy that moves adaptation before the codec while improving detection rate–accuracy trade-offs.Source: §5 · p. 14
A Runtime Analysis
The runtime analysis isolates SCOPE’s preprocessing cost and finds it small and stable across codec-family conditions and compression severities. The same conditional network handles all operating points without codec-specific weights or increased inference cost.
Runtime measurement
- The benchmark uses batch size one on CUDA and measures 75,000 pre-editing passes over COCO 2017 validation images.It tests three codec-family conditions at five severity values, with 5,000 images per family–severity pair; image reading is timed separately and excluded.Source: A Runtime Analysis · p. 20
Runtime by codec-family condition
- Total pre-editing takes about 11.81 ms per image (approximately 85 images per second), including tensor preparation and conditioning setup.The forward pass alone takes about 7.93 ms per image (approximately 126 images per second); Table 4 excludes codec execution, detector evaluation, image reading, and output saving.Source: A Runtime Analysis · p. 20, Table 4 · p. 20
- Codec-family conditions introduce very small runtime differences because the same network architecture serves every condition.SCOPE adds a small, stable preprocessing overhead across codec-family conditions and severity levels.Source: A Runtime Analysis · p. 20
Runtime by severity
- Runtime remains nearly constant across severity values because severity changes only the conditioning vector, not the pre-editor’s computational graph.The reported small variations are within normal measurement noise and do not indicate systematic severity dependence.Source: A Runtime Analysis · p. 20, Table 5 · p. 21
Runtime implications
- Changing family and severity conditions lets the same model cover compression settings without modifying its architecture or loading codec-specific weights.The reported inference cost does not increase across operating points.Source: A Runtime Analysis · pp. 20–21
B Semantic Decomposition of mAP Gains
The semantic decomposition breaks the overall COCO mAP difference into contributions from official supercategories, using category-level AP differences and the full category set as denominator. Across BMSHJ and BPG, gains are clearest at medium and high compression, while low-compression results are near-neutral or slightly negative and should be read alongside the rate–accuracy curves.
Decomposition method
- The decomposition aggregates individual COCO-category AP differences by official supercategory, using the full COCO category count as denominator so contributions sum to the overall mAP difference.It breaks down the global COCO mAP variation rather than reporting independent AP values for each supercategory.Source: B Semantic Decomposition of mAP Gains · p. 21
- A supercategory’s contribution is its summed category-level AP difference divided by the total number of COCO categories, not by the number in that supercategory.Positive and negative bars indicate contributions to the overall mAP change; an individual negative bar can occur even when total mAP improves if all contributions sum positive.Source: B Semantic Decomposition of mAP Gains · pp. 21–22
Scope
- The semantic plots cover BMSHJ and BPG, not JPEG, because same-quality JPEG baseline and SCOPE bitrates can differ substantially and confound semantic effects with bitrate shifts.JPEG remains included in the global rate–accuracy analysis.Source: B Semantic Decomposition of mAP Gains · p. 22
Findings
- At low compression, effects are near zero or mixed: BMSHJ is close to neutral for most detectors with a slight RT-DETR decrease, while BPG is slightly negative for all detectors.These same-quality results are interpreted as comparable to the baseline, not as a failure mode.Source: B Semantic Decomposition of mAP Gains · p. 22
Interpretation
- At low compression, a small negative or near-zero same-quality contribution can coexist with comparable detection accuracy at lower bitrate in the main rate–accuracy curves.The baseline may already preserve most detector-relevant information in this regime.Source: B Semantic Decomposition of mAP Gains · pp. 22–23
Findings
- At medium and high compression, BMSHJ and BPG show positive contributions across multiple groups, with BMSHJ’s largest and most consistent gains at high compression.Food, animal, electronics, furniture, kitchen, and indoor groups recur as positive contributors; sports, outdoor, and vehicle vary more across detectors and settings.Source: B Semantic Decomposition of mAP Gains · pp. 22–23, Figure 7 · p. 24, Figure 8 · p. 25