Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
Authors: YU CHENYUE
Lingao Xiao
Jinhong Deng
Ivor Tsang
Yang He
Venue: ICLR 2026 Poster (2026)
Keywords: dataset pruning
dataset compression
quantization
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large-scale image datasets are fundamental to deep learning, but their high storage demands pose challenges for deployment in resource-constrained environments. While existing approaches reduce dataset size by discarding samples, they often ignore the significant redundancy within each image -- particularly in the color space. To address this, we propose Dataset Color Quantization (DCQ), a unified framework that compresses visual datasets by reducing color-space redundancy while preserving information crucial for model training. DCQ achieves this by enforcing consistent palette representations across similar images, selectively retaining semantically important colors guided by model perception, and maintaining structural details necessary for effective feature learning. Extensive experiments across CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K show that DCQ significantly improves training performance under aggressive compression, offering a scalable and robust solution for dataset-level storage reduction.

Published as a conference paper at ICLR 2026
DATASET COLOR QUANTIZATION: A TRAINING- ORIENTED FRAMEWORK FOR DATASET-LEVEL COM- PRESSION
Chenyue Yu', Lingao Xiao Jinhong Dengl.24, Ivor W. Tsang 1:2,, Yang He
CCFAR, Agency for Science, Technology and Research, Singapore
IHPC, Agency for Science, Technology and Research, Singapore
Natioona University of Singapore
*University of Electronic Science and Technology of China (UESTC)
Nanyang Technological University (NTU), Singapore
fe1143627, xiao xi lingaa] @u. nus edd,, jhdengvisicne@mall com [Ivor. Tsang, He He_ Yang) ea-star- edu.
ABSTRACT
Large-scale image datasets are fundamental deep learning. but their high storage demands pose challenges for deployment in resource-constrained environments. While existing approaches reduce dataset size by discarding samples, they often ignore the significant redundancy within each image particularly in the color space. To address this, we propose Dataset Color Quantization (DCQ), a unified framework that compresses visual datasets by reducing color-space redundancy while preserving information crucial for model training. DCQ achieves this by enforcing consistent palette representations across similar images, selectively re- taining semantically important colors guided by model perception, and maintain- ing structural details necessary for effective feature learning. Extensive exper- iments across CIFAR-10, CIFAR-10 100, Tiny-ImageNet, and ImageNet-11 1K show that DCQ significantly improves training performance under aggressive compres- sion, offering a scalable and robust solution for dataset-level storage reduction.
INTRODUCTION
The rapid growth of datasets has been pivotal to the success of deep neural networks (DNNs) across diverse applications (Deng al.) 2009]. However, storing and training such datasets requires sig- nificant storage space and computing resources, which poses challenges for deployment on resource- constrained devices such edge servers, drones, and industrial platforms (Mao et al.) 2017). To alleviate this burden, dataset compression methods such as dataset pruning Yang et al. 2022 and dataset distillation (Wang al. 2018) have been widely studied. These approaches effectively re- duce the number of training samples while maintaining recognition performance, yet they do not explicitly address the per-sample storage overhead that arises from representing full-color images.
In practice, image storage and transmission costs are often dominated by color-space redundancy. For example, images collected from remote monitoring or drone surveillance typically undergo color quantization to reduce bandwidth consumption. While color quantization (CQ: Heckbert (1982)) has been studied extensively for image compression and visualization (Puzicha et al. (000), exist- ing methods fall short in dataset-level training scenarios. Specifically, CQ can be categorized into two types: 1) Image-Propertte based CQ: Preserving human visual perception according to the intrinsic properties of images by mapping the original color space reduced set of representative colors Ozturk et al.) 2014); and 2) Model-Perception-basee
CQ: Retaining recognizable features to enhance recognition accuracy by pre- trained neural networks (Hou et al. 2024).
Image-property. property- based CQ lacks neural network guidance, leading to two issucs: (1) ambiguous semantic boundaries and insufficient color contrast (Kimchi & Peterson 2008) that hinder discrim-

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(a) Original Image
(b) PrineePropeety-oooe CQ (K-Means)
(c) Model-Perceptiinn Perception-based CQ (ColorCNN)
(d) Ours (DCQ)
Figure I: Visualization of different Color Quantization algorithms on Tiny-Imagetie Images are quantized into 4 colors, which
color bits. (a) Original images. (b) Color quantization is performed through K-Means clustering to obtain representative color palettes for each image, wast- ing bits backgrounds. (c) Independent and representative color palettes obtained by ColorCNN, which have abrupt textural discontinuities. (d) Our DCQ assigns more colors to foregrounds and has less textural discontinuity.
inative feature learning: (2) uniform bit allocation across all colors, which wastes capacity on back- ground while underrepresenting critical foreground features (Figure 1b).
In contrast, but often introduces abrupt texture and edge discontinuities that distort visual features and degrade training performance (Geirhos et al. 2018). For instance, ColorCNN (Hou et al.] (2220) quantizes CIFAR-10 images into colors (2 bits) and achieves 77% accuracy under pre-trained R yet training on the quantized dataset yields only 58% accuracy due to distorted textures (Figure Tc
based CQ employs proxy networks to maintain recognition accuracy,
To address these limitations, we propose unified Dataset Color Quantization (DCQ) framework that compresses datasets by reducing redundant color information while preserving both semantic and structural fidelity. Unlike existing methods that quantize images independently optimize only for inference performance, our approach jointly considers dataset-level consistency, model-un aware color significance, and visual structure preservation. Specifically, DCQ clusters images with similar color distributions to enable shared palette learning, prioritizes important regions based model perception to retain critical semantics, and maintains texture continuity to avoid loss of structural in- formation. This unified strategy achieves compact, quantization-awwen datasets that remain effective for downstream training.
Our main contributions are summarized as follows: 1) To knowledge, this is the first work to propose a solution using a limited set of color palettes to represent datasets, aiming to reduce storage requirements and enable training color-restricted devices. 2) We introduce dataset-level color quantization algorithm that combines cluster-shared color palettes, attention attention-guided bits allocation, and peeleeepeeervinggg optimization. 3) Extensive experiments various datasets, including CIFAR- 10, CIFAR-100, Imageem, and ImageNet- 1 K, have validated the effectiveness of our method.
2
RELATED WORKS
2.1 COLOR QUANTIZATION
Color quantization reduces the number of colors used while preserving visual fidelity in RGB space. For maintaining human visualization, cluster algorithms such as MedianCut (Heckbert) 1982). OC- Tree (Gervautz & Purgathofer, 1988). and K-Means (Cheng & Weil 2019) are common cluster algo- rithms used to group similar colors and represent each group with single color to find the most rep- resentative color palettes; For maintaining neural network's recognition accuracy, ColorCNN (Hou et al. 2020) utilizes autoencoder to identify significant colors through end-to-end training processing for each image. ColorCNN+ (Hou et al. 22244) enhances traditional color quantization by combining deep learning and clustering techniques. CQFormer (Su et al. 2023) performs color quantization by mapping input images to quantized color indices through its Annotation Branch, while its Palette Branch identifies key colors in the RGB space. Although these algorithms work well with neural networks and preserve human visual recognition, they do not effectively optimize the train-set for better model performance.
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2.2 DATASET PRUNING
Dataset Pruning, also known as Coreset Selection, aims to shrink the storage of dataset by se- lecting important samples according to some predefined criteria. GraNd/EL2N (Paul et al., 2021) quantizes the importance of a sample with its gradient magnitude. TDDS Zhang et al. (024)) uses dual-depth strategy to achieve a balance between incorporating extensive training dynamies and identifying representative samples for dataset pruning. CCS (Zheng et al. (022) proposed a stratified sampling strategy to increase the classification accuracy in high pruning ratio. Entropy (Coleman et al., 2019) proposes selecting the highest-entrope examples for the coreset, as entropy captures the uncertainty of training examples. Forgetting (Tonvaa et al. 2018) refers to the number instances where example is misclassified after it has previously been correctly classified during model train- ing and AUM (Pleiss et al.) 2020) is data difficulty metric that identifies mislabeled data. However, these algorithms experience significant accuracy drop at high compression rates, necessitating al- ternative methods for effective dataset compression under such conditions which mcans we need to pioneer new direction to solve this problem, not just reduce the storage by removing images.
METHODOLOGY
3.1 MOTIVATION
Dataset pruning and distillation reduce sample counts to lower storage and computation costs, but often overlook per-sample redundancy. In images, storage and transmission are dominated by color redundancy: many pixels share nearly identical colors, especially in smooth regions (e.g., skies, walls) or gradual textures. For instance, a 256x256 RGB image has over 65k pixels but only a few thousand distinct colors. Thus, much capacity is wasted on subtle, semantically irrelevant varia- tions, motivating color quantization or palette-based compression to cut per-image storage while preserving semantic content.
To reduce the color-space redundancy, Color quantization (CQ) has been extensively employed in the contexts of image compression and visualization. However, existing CQ techniques are ill-suited for dataset-level training. Formally, we define color quantization as follows:
R.3 be the RGB color space. We define two Definition 1 (Color Space Distribution). Let S fundamental color distribution representations:
(i) Original Color Palette P [ps E S)) where each p; represents a unique RGB color vector in the continuous color space. (ii) Quantized Color Palette = {c, E 1R)E where k m, and each represents a representative color vector that preserves the most discriminative color features from the original distribution P. Here d denotes the dimensionality of the target color space. The mapping from P to C defines a color quantization function:
Q:P CC
where Q performs both cardinality reduction and potential space transformation while maintaining essential color characteristics. Given image I with original palette P. the quantized image IQ is obtained through:
Iq = Q(;CC: I C-+1 Iq
where Iq represents the reconstructed image using only colors from palette C.
Existing CQ methods can be broadly categorized into two paradigms: 1) Image-Property- Based CQ: This approach reduces color
color-space space redundancy by grouping chromatically similar pixels. While computationally straightforward, it is inherently limited in semantic preservation. Gradual color transitions spanning multiple objects or regions may cause semantically distinct areas to be er- roneously merged, resulting in a loss of structural information and critical boundary details. 2) Model--erccetiion Based CQ: These methods leverage pre-trained neural networks to guide color allocation (Hou et al.) 2020), ensuring high recognition accuracy for quantized images. However, they often introduce abrupt texture and edge discontinuities, which can distort visual structure and impair the learning of complete feature representations (Geirhos et al. 2018).
Crucially, both paradigms emphasize inference--rienned performance, optimizing the recognition of pre-trained models. Formally, given dataset D {(r4, N quantization function Q(-),
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Original Dataset
Color Clusters
Shared Color Palettes
Feature Extractor
Palette
Index
Storage
K
x X 201
Nx224x226x bis
y
N Num, of Images
Index
K
Training
Reconstruct Quantized Images
The pipeline of our dataset color quantization framework. First, apply K-mean clus- tering to group images based on their features extracted from pre-trained model. Next, within each cluster, perform K-means palette for all images the same cluster. The generated palettes and their corresponding indices are stored for later use. During training, retrieve the stored indices and palettes to reconstruct quantized images, which are then used to train neural network.
Figure 3:
the color palettes of individual images to generate shared color
and fixed pretrained model fø, inference-orientee quantization optimizes:
Q' arg max ((e.))=D(((((((())))).
(I)
where denotes the evaluation metric (e.g., accuracy).
In contrast, our dataset-level color quantization is to optimize the training of high-performance mod- els on quantized datasets. Formally, Drain and Dies denote the training and test sets. Quantization induces a training set DQ= {{Q (r),))) A model f trained on DQ is obtained as:
s (Q) arg min ((
The traiiig-orriented quantizer is then:
Qirann = arg max (( [A(f (Q))
(3)
To overcome these limitations, propose a novel Dataset Color Quantization (DCQ) framework, which integrates color quantization into dataset-level compression. Unlike con- ventional CQ methods that operate indepen- dently on individual images, DCQ jointly op- timizes palette sharing and semantic preserva- tion across the dataset, achieving substan- tial reduction in storage while retaining train- ing efficacy. Specifically, Chromaticity-Awant Clustering (CAC) strategy to aggregate images with chromatically simi- lar distributions into clusters and construct shared cluster-level palette instead of sample- wise color palette in previous CQ methods. Palette
(a) Original Image
(b) Feature Map from First Layer
first design
(c) Feature Map from Final Layer
Figure 2: Comparison of the original image and
Then, we propose an Attention-Guice Allocation mechanism that leverages model- derived attention to prioritize color represen- tation for semantically critical regions, ensur- ing the preservation of essential object features. Moreover, we leverage differentiable quantization to refine the color palette retain edge and tex- ture fidelity, preserving structural cues crucial for downstream training. Through these mechanisms, DCQ provides principled approach to dataset-level color compression, effectively minimizing dundant information while safeguarding both semantic and structural content indispensable for high- fidelity model training.
feature maps extracted from a ResNet-18 trained on CIFAR-10, Thermal color maps visualize ac- tivation strength from black to white. reflecting learned feature hierarchy.

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(a) Cluster
(b) Cluster
(c) Cluster 3
(d) Cluster 4
Figure 4: Visualization of four clusters obtained by applying ResNet-18 first-block features to par- tition CIFAR-IO into 20 clusters.
3.2 DATASET COLOR QUANTIZATION
The pipeline of the Dataset Color Quantization (DCQ) framework is shown Figure DCQ first groups images with chromatically similar distributions into clusters and constructs a shared cluster- level palette. Next, within each cluster, we perform K-means on the color palettes of individual images to generate shared color palette for all images in the same cluster. The generated palettes and their corresponding indices are stored for later use. During training, we retrieve the stored indices and palettes to reconstruct quantized images, which are then used to train neural network. In the following, we will give a detailed introduction the proposed method.
3.2.1 CHROMATICITY-AWAN CLUSTERING
Traditional CQ methods typically construct palettes independently for each image. This per-image quantization strategy allows the color palette to be optimized to the specific distribution of a sin- gle image, thereby minimizing quantization error within that image. However, such independence introduces inconsistency across the dataset: visually similar regions in different images may be mapped to disparate representative colors. As result, the color space perceived by the training model becomes fragmented, which increases the difficulty of learning stable semantic boundaries and weakens the model's generalization ability.
To overcome this limitation, we propose the use of shared palettes across multiple images. By quan- tizing a collection of images rather than on individual samples, the learned palettes achieve greater cross-image consistency. This consistency reduces the semantic ambiguity induced by palette mis- alignment and enables the model to perceive color distributions more stably. Nevertheless, con- structing single palette from a large of diverse images poses new challenge. When the overall color distribution becomes too broad, a fixed-size palette must accommodate a wider variety of colors. Consequently, the quantization error for individual images increases, the palette can longer precisely capture each image's I local chromatic distribution.
To this end, introduce Chromaticity-Awerr Clustering (CAC) mechanism that first partitions the dataset into groups of images with similar color distributions to balance the trade-off between cross- image consistency and quantization fidelity. Shared palettes are then generated within each cluster, allowing the method to retain image consistency while constraining the color variance that the palette must represent. this manner, quantization error remains controlled, and the resulting palettes better preserve semantic boundaries and structural features across images.
In particular, identify images with similar color distributions, we utilize semantic feature maps (z)),, which are the outputs of the i-th layer in neural network model M. These feature maps cap- ture hierarchical representations of an image, from low-level color patterns to high-level semantics. The resulting feature vector provides comprehensive representation of the image's 's color distribu- tion, enabling effective comparison across images. While deeper output feature maps (x(x) encode abstract semantic concepts like object parts and contextual relationships, shallow layers capture local patterns including color distributions (Zeiler Fergus 2014).
Through empirical analysis of feature distributions across layers, observe inherent trade-off:
as (Sem(1x) f (Vi((>)) $
(semantic abstraction)
(4)
(visual fidelity).
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where Sem(-) represents semantic information capacity and Vis(-) indicates visual fidelity, and the visualization of feature-map is shown in Figure 2 Based on this observation, we select the shallow
layer feature map as perceptual representation:
(M - Wshallow (2)
We partition the dataset using K-Means clustering on semantic features Wshallow (3) to create clusters. We set k = 20 through ablation experiments. As shown in Figure[4 this clustering not only groups images by dominant color distributions (blue, red, green, and gray tones) but also transcends class boundaries by grouping semantically distinct objects with similar chromatic properties.
Coheren SCOVE
EoPieer (CDs
CelaOws (CXPEM
Coartii (1P20)
OCTec
(a) CIFAR-10.
(b) CIFAR-100.
(c) Tmag-ImageNet.
Figure 5: Comparison between prior color quantization methods and our approach, evaluated with ResNet-18. Detailed results are provided in Appendix[B] Unlike the original paper, which quantized the test set while training on the original train set, we quantize the entire train-set and keep the test- set unchanged.
3.2.2 ATTENTION-GUDDED PALETTE ALLOCATION
Traditional color quantization approaches typically assume uniform importance across color palettes (Celebi 2023). However, different image regions contribute unequally semantic recogni- tion, and allocating bits uniformly across all regions may neglect critical structures. To address this, we introduce the notion of Color Palette Impact Level, which distinguishes palettes that are crucial for model performance from those that are less influential.
Definition 2 (Color Palette Impact Level). We categorize the roles of color palettes in model recog- nition into two distinct classes: (1) High-Impact Palettes (Pμ): These palettes significantly influ- ence model performance. Changing them to any other palette results in noticeable increase in loss. (2) Low-Impact Palettes ( (PI):: These palettes have little effect on model performance, There exists at least one alternative palette that can replace them without meaningful change in loss.
To identify high-impac palettes, we employ attention map generated from task-specific pre-trained models. The Grad-CAM++ heatmap reveals discriminative regions that contribute to the network's ss classification decision. The higher attention values indicate greater relevance to the network's prediction. For each image, we retain the top kara% % of pixels with the highest attention values, ensuring that only the most relevant regions are preserved, while all remaining pixels are set to zero. The value of kora % was determined through ablation studies in Appendix C.4
(Chattopadhay et al. 2018 to obtain an
The selected pixels from all images within a cluster are aggregated to form an expanded palette space. Following prior work (Orchard et al., [199]), we convert the RGB values of these pixels to LAB color space to better preserve perceptual similarity. Finally, K-means clustering applicd to this aggregated LAB palette space generate shared quantized palette that balances compactness and semantic fidelity across images.
3.2.3 TEXTURE- PRESERVED PALETTE OPTIMIZATION.
A key goal of color quantization to preserve important texture information. However, traditional K- K-Means clustering assigns pixels based solely color similarity, ignoring the image's structural

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details. Without an error feedback mechanism measure Texture Loss (TL), particularly in local gradients, edges, and spatial patterns, this approach often leads to texture degradation, especially in fine-detail regions. To address this, our algorithm refines K-Means K-Means-generatte color palettes through a quantizable optimization process, reducing Texture Loss (TT.) and better preserving local structural details.
Inspired by style transfer (Johnson et al. 2016), we designed a differentiable textural palette opti- mization model that performs differentiable color quantization by mapping each pixel in the image to its nearest color in palette, applying a straight-through estimator (STE) (iin et al. 2019) to enable backpropagation through the quantization process. For each cluster, after obtaining the shared color palette, we optimize the palette using gradient descent algorithm to minimize the loss function, ensuring that the color differences between the original and quantized images are minimized. We optimize the color palette by minimizing the edge distribution differences F. I. between the original image and image after color quantization which can be calculated using:
G(I) = V (I S2)² + (I Sy)?
(6)
EL = Σ my MSE (Glunng), G ((fguam))
1=1
where r'nng and Tqmam represent the i-th channel (L, A. B) of the original and quantized images, Se and Sy using Sobel operator (Kanopoulos et al. 1988) to calculate edge information for each channel, MSE stands for the mean squared error and w; is the weights for each channel and * stands for convolution, adjusting the contribution of each channel to the total loss. The result of using Texture-preserve Palette Optimization was shown as ablation experiments in Appendix C.I
Figure D shows the result of our dataset color quantization algorithm. Under 2-bit color quanti- zation, compared to traditional Image-Property-beee Property-based quantization, our approach achieves superior edge-texture preservation and palette allocation efficiency, resulting in enhanced structural integrity and detail fidelity. Compared to Model-Perceptioon perceptual fidelity with enhanced texture continuity.
quantization, our method better preserves
EXPERIMENTS
4.1 EXPERIMENT SETTINGS
In this task, the effectiveness of the proposed dataset color quantization is evaluated from popular datasets: CIFAR-10, 10, CIFAR- ImageNet-IK KK (Deng et al. 2009) with ResNet (He et al. 2016). Details of datasets are provided the Appendix A.I
100 (Krizhevsky et al.] 2009). Tiny-ImageNet (Le & Yang 2015) and
our algorithm, we used our color quantization algorithm on the original train-set and kept original test-set. Since our work tackles less-studied problem of dataset color quantization with no known clear solution, it is important to set an adequate baseline for comparison. We choose two aspects of the most relevant baselines to show the efficiency of our algorithms. (1) For color quantization, we choose ColorCNN (Hou et al. 2020). ColorCNN+ (Hou et al.] 2024). CQFormer (Su et al., 2023), MedianCut (Heckbert 1982) and OCTree (Gervautz & Purgathofer] 1988). We apply image color quantization algorithms to every image in the entire train-set while keeping the test-set unchanged. This differs from the original paper, which quantized the test-set while training on the original train-set. (2) For dataset pruning algorithms, EL2N (Paul et al.
202T. Entropy (Coleman et al. (019) and Forgetting (Toneva et al.) 2018). CCS (Zheng et al,
2022 TDDS (Zhang et al. 2024) are used as baselines. These algorithms use coreset from the train-set to train a model and evaluate on the original test-set. More information about baselines is provided in the Appendix A.2.2. The compression ratio achieved through color quantization can be formally defined in relation to the bit depth reduction. Starting from the standard 24-bit RGB color space (8 bits per channel). when quantizing to bits, the color palette is reduced to 2 distinct colors. The corresponding compression ratio gr is given by 4r = 1 - 9/24, establishing a direct mapping between the compression levels of color quantization and dataset pruning strategies.
Comparison to Color Quantization. Figure 5) shows the performance of using the color quantized dataset for training. Our performance substantially outperforms other methods from to 6 bits for all
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Table 1: Comparison of dataset pruning algorithms and our dataset color quantization algorithm on CIFAR-10, CIFAR-100 100 with ResNet- 18, and ImageNet- 1 with ResNet-34.
CIFAR-10
CIFAR-100
ImageNet-IK 3
Color Bits
3
2
5
4
2
1
5
Colors/Image
8
8
Prune Ratio
80%
83%
87.5%
92%
96%
80%
83%
87.5%
92%
96
80%
83%
87.5%
92%
96%
Random
88.15
84.38
80.
77. 07.04
70.08
57.36
50.96
42.19
39.71
36 68
62.54
18.18
53.34
50.32
20.28
Entropy
79.08
74.59
67.58
64.51
57.06
41.83
35.45
29.77
28.96
22.16
55.80
44.59
42.04
31.04
17.06
EL2N
70.32
67.06
25.99
21.31
19.85
15.51
12.51
10.36
8.36
7.51
31.22
21.28
13.99
11.69
9.81
AUM
57.84
49.09
29.58
25.60
21.35
16.38
11.83
9.45
8.77
5.37
21.12
15.09
10.13
8.93
6.35
CCSAUM
90.53
88.97
87.61
75.01
73.02
63.19
60.05
52.16
30.02
28.20 24
64.49
58.44
51.96
45.58
31.31
CCS
90.93
88.05
86.66
74.31
73.02
63.99
60.45
$1.86
31.12
27.33
65.01
58.74
57.57
46.28
30.44
CCSEL
89.81
88.47
87.01
74.77
73.17
61.33
60.75
52.16
32.07
28.17
64.71
58.15
56.54
45.19
29.14
TDDS (Strategy-E)
91.30
88.72
87.47
77.32
72.46
63.01
61.44
55.13
32.15
26.55
62.56
56.48
54.91
43.91
29.56
DCQ (Ours)
94.39
93.34
91,02
89.15
79.90
69.05
67.89
65.02
57.69
38.44
66.99
64.34
62.02
49.69
35.95
Full Accuracy
95.45%
78.21
73.54%
Table 2: Ablation Studies of Different Strategies.
Clusterigg Compurison.
(b)
CIFAR-ID clustering)
Attention Map Melhods (Second leval
chastring).
Bils Label Random
Image Final Shallow
Bils
3
40.10
28.44
68.41
42.10
79,90
1 cluster
71.02
84.52
86.71
$9.49
90.55
91.01
Bits
51.08
39.65
79.15
53.78
89.15
10 cluster
76.12
87.95
90.08
92.
92.79
93.69
None
72.02
84.02
86.79
89.09
91.02
79.90
94.89
GindAA
77.34
GradCAM++
79.42
70.29
89.29
80:44
94.39
100 clusier
76.94
36.61
90.91
91.83
92.66
93.11
RISE
79.04
88.91
92.85
93.30
6 81.99
75.52
00.52
83.93
94.89
150 closter
76.77
86.21
87.99
90.04
92.11
93.58
LayerCAM
80.66
89.71
90.95
93.09
three datasets including (a) CIFAR-10, (b) CIFAR-100, and (c) Iiny-ImageNet. Using 2-bit color palette (4 colors) for quantization, 10, 57.69% on CIFAR- 100, and 50.51% % on Iny-ImmgeNet. This represents a improvement over the ColorCNN (Hou et al. 2020) results, with gains of 30.00% (89.15% 59.15% CIFAR-IO, 35.37% (57.69% 22.32% on CIFAR-100 100, and 18.06% (50.51% 32.45%) on Imay-lmageNet.
method (DCQ) achieves accuracies of 15% CIFAR-
Comparison to Dataset Pruning. Table presents the performance comparison of various dataset pruning methods against our dataset color quantization (DCQ) algorithm. DCQ consistently outper- forms other methods across all pruning ratios and datasets. For instance, under 96% compression ratio, we apply 1-bit quantization (2 colors per image), clustering the entire dataset into 20 clusters. As result, only 40 distinct colors are used quantize the entire train-set. DCQ achieves accura- cies of 79.9 79.9% on CIFAR-10 10 and 38 18.44 on CIFAR-100 100, significantly surpassing the CCS results of 73.02% and 28. 24., respectively. These results highlight DCQ an effective color quantization strategy, particularly under high pruning ratios. Similarly, DCQ excels on ImageNet- K under high pruning ratios.
4.2 ABLATION STUDIES
How Many Clusters? Table 2b presents the impact of varying the number of clusters in the initial K-Means clustering step. Using the least number of clusters, which is cluster, extracts all color palettes get a shared quantized color palette for every image in the dataset. On the other hand, the most number of clusters (i.e., 50,000 clusters for CIFAR-10) 10) means that we assign each image a unique quantized color palette, which can be seen Image-Property based CQ. The best performance achieved with 20 clusters.
Table 3: Comparison of dataset pruning algo- rithms and our algorithm CIFAR-10 with ResNet-34 and ResNet-50.
ResNes 34, CIEAR 10
Reshie 50, CIFAR 10
80%
85%
$7.5%
92%
9696
80S
83%
87.5%
92%
96%
Randon
79.19
29.98
57.84
29.58
CCSAUM
87.53
TODS
39.58
86.15
56.93
73.05
90.68
87.62
74.01
70.45
Ours
94.39
93.14
91.15
39.47
79.87
94.19
92.94
90.55
88.27
77.26
Which Feature to Use? Table 2a compares clustering performance across different feature extrac- tion approaches: (1) label-based grouping that clusters images within the same class; (2) randomly
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assign images into different clusters; (3) direct image clustering that applies K-Means raw pixel values; (4) final-layer feature maps from the final residual block and (5) shallow-layer feature maps from the first residual block the different baselines. The shallow-layer features consistently out- perform all alternatives, which aligns with our analysis in Sec. 332.1 Table 2a shows that clustering with Shallow-layer Feature maps achieves the best results compared other features.
Which Method to Get Attention Maps? Table 2c compares different attention map genera- tion methods, including Grad-CAM (Selvaraju et al. 2016), LayerCAM (Jiang et al., [02T), and RISE (Petsiuk et al. 2018). Our algorithm is guided by attention maps and is not limited to Grad- CAM++; other methods are equally applicable.
20
90
80
28 10
30
200 spects
30
200 spocts eocchs
200 opoc's
50 opopehs
SOTA
SOTA
eoochs
25 epochs
30 spopps
eoochs
ezochs
507A 72 40%
50TA 77.30 37.3%%
50TA 518
25
50
100 Epoch
125
150
375
205
50
75
1.0
125
150
175
200
50
75
100
125
150
375 200
(a) 1 Bit (2 colors).
(b) 2 Bit (4 colors).
(c) 3 Bit (8 colors).
Figure 6: Illustration of the test accuracy of our method using ResNet-18 on CIFAR-10.
4.3 ADDITIONAL ANALYSIS
Training Efficiency. Figure O illustrates a key limitation of traditional dataset pruning. Although pruning reduces the dataset from N aN samples (a < 1), the total number of parameter updates remains nearly unchanged to ensure convergence. With batch size b, the original dataset requires N/b updates per epoch, while the pruned dataset requires N/(ab) to match optimization steps. This compensatory mechanism cancels potential computational gains, so reduced dataset size does not improve training efficiency. Hence, our comparison the same compression ratio in Table T is fair.
Table 4: Combining color quantization and dataset pruning for extreme compression ra- tio. The pruning rate is 90%, and color quan- tization is --bit.
Table 5: Comparison of dataset distillation algorithms and our algorithm on different datasets using ResNet- 18. Entries with OOM denotes Out of Memory.
CIAR 10
CIFAR 100
CIFAR-I0
Tiny-ImageNaa ImageNet-IX
Color Biis
3
2 5
8
IPC=50 (R- IPC=50
Total Rabio
98.0%
98.41
98.74
99.24
98.0%
98.4%
98, 18.7%
99.2%
Random
BLAN
55.13
50.48
46.19
31.44
23.34
19.18
15.47
4.33
DM
63.0
71.6
53.9
00M
28.0
00M
00M
00M
00M
CCSATTM
22.58
20.11
SRc2L
47.5
27.2
49.7
41.1
52.8
46.8
63.28
55.18
46.92
15.00
G-VBSM
59.2
53.5
51.0
47.6
55.7
51.8
Ours e CCSA454
79.92
79.53
76.01
70.73
46.26
40.22
39.49
30.94
Ours
70.9
67.4
57.6
50.5
59.6
54.4
Network Generalization. Table 3 shows that our algorithm outperforms others on larger networks (ResNet-34, ResNet-50), demonstrating strong transferability across architectures. Results for addi- tional networks are provided in Appendix C.2
Combine with Dataset Pruning and Comparison with Dataset Distillation. Since our method is
orthogonal to dataset pruning, it can be combined with pruning to achieve higher compression. Ta-
ble 4rports results of integrating CCS-based coreset selection (10% retention; [hheng et al. (2022) ) with q-bit color quantization, reaching extreme ratios up to 99 99.2% (70.73% accuracy on CIF, which highlight the effectiveness of hybrid strategies for extreme data compression. For dataset dis- tillation, we use G- G-VBSM (Shao et al. 20244,, SRe2L (Shao et al.) 2024), MTT (Cazenavette et al.) 2022). and DM (Cazenavette et al.J 2022) as baselines. To match the compression rates Shao et al. (2024), we apply CCSAUR AUM with pruning ratio r% and quantize the coreset to g bits. For CIFAR- 10 (IPC = 10, 99.8% compression; IPC = 50, 99%), we set (r%, q) to (95%, 1) and (75%, 1). For ImageNet (IPC 50, 90%; IPC 100, 80%), we set (r%,q) to (0%, 5) and (0%, 2). For ImageNet- IK (IPC = 50, 96.1%; IPC = 100, 92. 1)), we set (r%, q) to (50%, 2) and (60%, 5). This maintains consistency with the original paper. Table shows our algorithm outperforms others.

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Additional Analysis and Visualization. Additional analysis is provided in Appendix D and visu- alizations of all datasets are provided in the Appendix
CONCLUSION
We present novel dataset condensation approach that leverages color quantization reduce stor- age, improving data efficiency and enabling new forms of compact data representation. While ef- fective, our work has several limitations. Future research could investigate adaptive, per-image quantization strategies to balance compression and accuracy more flexibly. In addition, developing neural architectures specifically optimized for color-quantized data, rather than full-color inputs, may further enhance performance on compressed datasets. These directions highlight the potential of integrating quantization with dataset-level learing to advance efficient and robust deep learning.
ACKNOWLEDGEMEET
This research is supported by A*STAR Career Development Fund (CDF) under Grant C24351201 1, the National Research Foundation, Singapore under its National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-202-0003, Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
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A EXPERIMENTS SETUP
A.1 DATASETS
CIFAR-I0/IFAR-L0O are datasets that consist of 10 and 100 classes, respectively, with image resolutions of 32 x 32 pixels. We use ResNet-18 architecture (He et al.] 20T6) to train the models for 40,000 iterations with batch size 256, equivalent to approximately 200 epochs. Optimization is performed using SGD with a momentum of 0.9, weight decay of 0.0002, and an initial learning rate of 0. A cosine annealing scheduler (Loshchilov & Hutter 2016) applied with a minimum learning rate of 0.0001. Data augmentation includes 4-pixel padding crop and random horizontal flips. The experimental setup follows that of (Zheng et al. 2022).
containing 200 classes. Each class consists of 500 medium-resolution images, and the spatial size of the images is 64 x 64 pixels. A ResNet-18 architecture (He al., 2016) is used, and models trained for 60 epochs with batch size of 128. Optimization is performed using SGD with a momentum of 0.9, a weight decay of 0.0002, and initial learning rate of 0. A cosine annealing scheduler (Loshchilov & Hutter (0166) is applied with minimum learning rate of 0.0001. Data augmentation includes an 8-pixel padding crop and random horizontal flips. The experimental setup follows that of (Hou al., 2020).
Finy-ImageNet-2000 is subset of the ImageNet-IK dataset (Le & Yang 2015
ImageNet-1K is dataset containing 1,000 classes and 1,281,167 images in total. The images are resized to a resolution of 224 x 224 pixels for training. ResNet-34 (He et al., 2016) is adopted as the network architecture. Models are trained for 300,000 iterations with batch size of 256, corresponding to approximately 60 epochs. Optimization uses SGD with momentum of 0.9, weight decay 0.0001, and an initial learning rate of 0. cosine annealing learning rate scheduler is employed. The experimental setup follows that of (Zheng et al. 2022).
A.2 BASELINES
A.2.1 DATASET PRUNING
Random randomly selects partial data from the full dataset to form coreset.
Entropy (Paul al. 202T) quantifies sample uncertainty. Samples with higher entropy are consid- ered to have greater influence on model optimization. The entropy is computed using predicted probabilities the conclusion of training.
Forgetting (Toneva et al. 2018) measures the frequency of forgetting events during training. Sam- ples that are consistently remembered can be removed with minimal impact on performance.
CCS (Zheng et al. 2022)
applies stratified sampling variations based importance scores to en- hance coreset coverage. This method can also integrate other criteria to further improve results.
AUM (Pleiss et al. defined as the probability gap between the target class and the next largest class across all training epochs. A larger AUM suggests that sample is of higher importance.
2020 selects samples with the highest area under the margin. The margin is
EL2N (Paul et al. 202I) selects samples based on larger gradient magnitudes, which can effec- tively approximated by error vector scores. For practical evaluation, only the average of the first 10 epochs' eтror vector scores is considered.
TDDS /Zhang et al. 2024 first captures each sample's 's contribution throughout training to integrate detailed dynamics. then analyzes the variability of these contributions to identify well-generalized samples.
A.2.2 COLOR QUANTIZATION
ColorCNN (Zhang et al. 2024) preserves critical image structures and produces quantized version with similar class activation maps to the original image. During training, non non-differentiable parts are approximated, and regularization is used to maintain similarity and prevent premature convergence. The ColorCNN is trained end-to-end with classification loss, incorporating color jittering to improve robustness.
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ColorCNN+ (Hou et al. 2024) is extended model that supports multiple color space sizes by reducing feature dimensions from a predefined number D to the desired color space size C using channel-wise average pooling. Unlike the original ColorCNN, which adapts the network structure based on the color space size.
CQFormer (Su et al. 2023) is model designed for color quantization using dual-branch architec- ture. The annotation branch processes feature activation using a UNeXt network, while the palette branch generates color palettes through Palette Attention Module (PAM) with reference queries. The model operates across training and testing stages, incorporating intra-cluster color similarity regularization enhance color classification and detection capabilities by learning compact color representation.
MedianCut (Heckberi) 1982) is technique used reduce the number of colors an image while preserving its overall visual appearance. The algorithm works by recursively dividing the color space into smaller regions (bins) based on the distribution of pixel colors.
Timy ImageNet
Color
Coler
OCTree
57.91
66.95
75.12
77.24
50.29
$4.99
27.93
30.15
39.02
43.34
47.39
$2,89
24.63
26.89
34.76
40.12
46.16
49.45
$0.22
+0,63
=0.71
20.03
20.75
20.25
40.42
10.45
40.52
20.56
20.51
30.65
40.38
20.41
20.48
10.53
+0.57
=0.02
CQFocmer
49.52
75.49
20.01
29.95
19.91
20.03
29.95
20.63
=0.53
ColoCNN+
48.77
49.66
59.92
68.34
72.90
K2.89
18.53
19:52
27.92
29.34
36.49
40.99
15.21
16.35
24.53
26.12
33.25
37.55
+0.47
+0.48
+0.56
40.69
+0.67
40.75
+0.33
+0.34
+0.42
+0.44
+0.49
+0.92
40.31
+0.32
+0.38
+0.41
+0.46
+0.49
ColaCNN
46.12
39.48
70.23
90.34
$4.49
96.89
16.54
22.81
34.93
38.64
40.19
45.39
13.28
19:42
31.63
35.21
36.89
42.11
=0.72
=0.77
+0.37
10.48
=0.51
+0.29
+0.34
10.45
=0.54
MalisnCar
52.56
$0.65
10.71
+0.74
60.76
40.75 76
40.81
40.44
10.46
40.54
60.61
60.63
60.67
60.41
80 46
60.51
10.58
60.59
20.61
Ours
79.94
89.15
91.82
93.34
94.39
94.89
38.44
57.69
65.02
68.89
70.31
71.55
36.44
58.51
52.12
55.09
57.61
60.02
60.72
+0.33
20.54
40.86
20.87
20.88
40.51
+0.67
40.73
20.76
20.78
40.79
40.49
40.02
20.04
10.06
20.08
20.71
Table 6: Performance comparison for different methods across varying color bits on CIFAR-10. CIFAR-100, and Tiny-lmageNet datasets. Mean and standard deviation are computed from 5 inde- pendent runs.
OCTree (Gervautz & Purgathofer 1988) builds an octree data structure where each node represents color group of similar colors. Starting with all colors of image in the octree, the process iteratively merges nodes (colors) that are closest together until a target number of colors is reached.
All images randomly sampled from the train-set ensure representativeness. The visualization demonstrates how our method progressively reduces the color palette while maintaining the visual fidelity of the original images. As the quantization ratio increases (i.e., fewer color bits), ob- serve that our approach effectively preserves key structural details and salient features, even under aggressive color reduction scenarios.
B ADDITIONAL INFORMATION
B. PRIMARY TABLE FOR COLOR QUANTIZATION
Table O shows the results of color quantization on different datasets. We ran each experiment five times calculate the mean and standard deviation. The experimental results demonstrate that our color quantization algorithm outperforms other color quantization algorithms in improving the per- formance of the train-set after color quantization.
C ADDITIONAL EXPERIMENTS
C.I ABLATION OF SOBEL OPERATOR
Table demonstrates that incorporating edge distribution minimization consistently improves the color quantization performance across bit depths (1-5) on CIFAR-10. Notably, we observe per- formance gains ranging from 2.26% to 3.10%, with the most significant improvement occurring
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1-bit quantization (76.8% - 79.90%). These results validate that preserving edge information during quantization leads to better overall performance.
Table 7: Effect of using minimizing the edge distribution differences on CIFAR-1O.
Color bit
4
No using using 79.90
76.80
87.05
89.22
91.33
92.13
89.15
91.02
93.34
94.39
C.2 GENERALIZATION TO DIFFERENT NETWORKS
Table 8 presents a comparative analysis of our proposed algorithm against existing dataset pruning methods across Shufflenet (Zhang et al. 2018) and MobileNet-v2 (Sandler et al., (018) architec- tures. The empirical results demonstrate that our approach consistently outperforms baseline meth- ods across different compression ratios, achieving superior accuracy on both network architectures.
Table Comparison of dataset pruning algorithms and our algorithm on CIFAR-I0 with ShufffleNet and MobileNet-v2.
ShufffleNet, CIFAR-10
MobileNet-v2. CIFAR- 10
80%
83%
87.5%
92%
96%
80%
83%
87.5%
92%
96%
Random
83.05
80.29
72.18
70.34
60.18
81.25
78.09
70.38
68.04
57.88
EL2N
69.20
65.32
23,68
22.13
20.19
67.20
63.02
21.88
20.13
18.19
AUM
54.64
45.49
27.78
20.62
17.35
55.84
46.59
27.78
23.30
19.05
CCSAUM
82.14
80.32
77.51
65.94
63.45
80.64
78.02
75.01
63.44
61.15
TDDS
83.08
81.65
80.42
66.55
62,98
81.28
79.15
77.92
65.05
59.48
Ours
89.9
87.98
86.31
83.67
74.41
91.19
88.84
89.25
85.17
76.26
Table 9 shows comparative analysis of our proposed algorithm against existing dataset pruning methods across Swin Transformer (Liu et al., 2021 and ViT-Small (Lee et al. 2021).
Table Comparison dataset pruning algorithms and our algorithm on Transformer- Based Model. For CIFAR-10, 10, we tested our algorithms on Swin Transformer; For CIFAR-100. we tested our algorithms on ViT-Small.
CIFAR-10
CIFAR-100
80%
83%
87.5%
92%
96%
80%
83%
87.5%
92%
96%
Random
82.26
79.18
77.09
69.27
64.18
33.83
30.96
26.19
25.61
22.68
EL2N
59.13
55.15
27.77
24.97
21.64
22.49
14.55
11.67
9.18
8.76
AUM
65.61
56,51
28.65
23.22
20.17
22.58
15.32
12.88
10.15
6.08
CCSAUM
84.38
80.82
75.18
71.22
67.01
59.18
55.35
51.16
27.13
22.84
Ours
87.66
84.91
80.82
76.83
70.54
68.38
65.19
63.72
47.19
35.42
C.3 ABLATION OF FEATURE EXTRACTION ALGORITHMS
During the Cluster for Color Perceptual Consistency step, used pre-trained model to extract features. Table 10 compares the clustering performance of different features, including PCA, Gray Level Co-occurrence Matrix (GLCM). Color Coherence Vector (CCV), RGB Color Histograms, and Shallow-layer Feature Maps. The results show that clustering with Shallow-layer Feature Maps achieves the best performance.
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Table 10: Different feature extraction strategies on CIFAR-I0 trained ResNet-18.
Color Bits
1
2
3
4
5
PCA
50.11
61.78
70.23
73.45
78.47
Gray Level Co-occurrence Matrix
70.15
83.17
85,49
86.17
90.75
Color Coherence Vector
74.15
86.97
87.19
89.77
92.35
RGB Color Histograms
75.95
86.18
88.49
90.17
91.45
Shallow-layer Feature Map
79.90
89.15
91.02
93.34
94.39
Table 11 presents the results using different layers for feature extraction. Specifically, we evaluate Shallow-layer feature maps from the first residual block, Mid-layerl feature maps from the second residual block, Mid-layer2 feature maps from the third residual block, and Final-layer feature maps from the final residual block. The results indicate that Shallow-layer feature maps achieve the best performance.
Table 11: Comparison of Different layers CIFAR-10 trained on ResNet-
Color Bits
1
3
4
Mid-layer2 feature maps
69.15
79.81
81.09
83.76
87.75
Mid-layerl feature maps
74.15
86.97
87.19
89.77
92.35
Final-layer Feature Map
42.10
53.78
66.39
75.15
80.44
Shallow-layer Feature Map
79.90
89.15
91.02
93.34
94.39
C.4 ABLATION OF GRAD-CAM+ PERCENTAGE
Table 12 presents the performance under different indicate that preserving 50% of the pixels achieves the best performance.
retention percentages. The results
Table 12:
Utilizing Grad-CAM++ attention maps to maintain the pixel distribution ratio kGra % on CIFAR-10 and ResNet-18.
Color Bits
2
3
4
5
80%
75.95
87.17
88.1
89.98
93.05
20%
77.31
87.79
88.23
90.55
91.47
70%
76.15
86.97
87.41
90.99
92.55
60%
76.85
87.17
88.89
91.97
93.01
30%
77.95
88.18
89.19
92.17
93.45
50 %
79.90
89.15
91.02
93.34
94.39
D ADDITIONAL ANALYSIS
D. EXPERIMENTS IN OTHER TASKS
Table 13a and Table 13b show that our algorithm outperforms other dataset pruming algorithms on
both image segmentation and object detection tasks under high compression ratios.
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Table 13: Comparison of different dataset pruning algorithms on MS COCO.
(a) Image Segmentation (MS COCO), Full dataset
(b) Object Detection (MS COCO), Full dataset AP
AP50 53.20%.
58.30%.
Method
80% %
87.5% %
92 %
96%
Method
80 %
87.5%
92 %
96%
Random
45.60
42.07
38.15
25.99
Random
48.80
44.07
40.15
31.89
CCS
43.68
39.91
35.58
26.46
CCS
46.71
42.89
37.08
30.15
EL2N
38.98
36.12
29.91
24.26
EL2N
40.05
39.15
31,48
27.36
Ours
47.38
44.29
41.68
28.77
Ours
50.78
46.29
42.08
33.37
Table 14 shows that algorithm outperforms other dataset pruning algorithms generative tasks.
Table 14:
Comparison of dataset pruning algorithms on the CIFAR-10 PSLD generative
task (Pandey & Mandt 2023). Results are evaluated by FID (L), where lower values indicate better performance.
Method
80%
87.5%
92%
%
Random
39.4
50.2
119.8
CCS
15.7
37.8
52.8
121.1
EL2N
18.1
45.1
61.4
138.3
Ours
11.9
21.3
42.2
113.5
D.2 COMPARISON WITH OTHER TASKS
Table 15a
Table 15b and Table 16 shows comparison with other dataset compression algorithms
like DUAL Cho al. 2025). DQ Zhou al. 2023). DQAL (Zhao et 2024). ADQ (Li al.
2025 and AutoPalette Yuaan et al. 2024). our algorithms outperform than other dataset pruning
algorithms.
Table 15: Comparison of our DCQ with different dataset compression algorithms.
(a) CIFAR-10 (ResNet-18)
(b) ImageNet-Ik (ResNet-34)
Method
80%
87.5% %
92% %
96%
Method
80 %
87.5%
92%
96%
ADQ
90.40
87.51
86.32
75.99
ADQ
65.31
60.91
47,02
32.19
DQAL
90.20
87.61
85.30
75.80
DQAL
62.21
60.41
46.73
31.89
DQ
89.40
87.70
84.25
77.92
DQ
64.30
60.45
47.17
32.27
DUAL
91.42
88.99
86.15
74.83
DUAL
66.50
61.15
46.55
33.83
Ours
94.39
91.02
89.15
79.90
Ours
66.99
62.02
49.69
35.95
Table 16: Comparison between algorithm and AutoPalette CIFAR-10 and CIFAR-100 (Con- vNetD3)
CIFAR-10
CIFAR-100
IPC=10
IPC=50
IPC=10
IPC=50
AutoPalette
74.3
79.4
52.6
53.3
Ours
77.9
82.3
58.9
61.7
Table shows that standard data augmentation further improves performance when training on our color-quantized datasets.
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Table 17: Combining other dataset augmentation algorithms on CIFAR-I0 (ResNet-18) under 1-bit quantization.
Method
80%
87.5% %
92%
96% %
Original setting
94.39
91.02
89.15
79.90
Original setting + ColorJitter
95.02
92.44
90.45
80.95
Original setting GaussianBlur
95.47
92.95
91.47
81.75
Table 18 shows that our algorithm outperforms other algorithms on fine- fnn-grained recognition tasks.
Table 18: Different dataset pruning algorithms on a fine-grained recognition task (Stanford Cars,
API-Net (Zuang et al. 2020)). Full dataset performance 95.3%.
Method
80%
87.5% %
92%
96 %
Random
85.95
71.28
68.19
64.55
CCS
88.98
74.17
70.07
67.17
EL2N
67.32
23.98
16.31
14.85
Ours
91.16
76.22
72.34
70.17
Table 19 shows that our algorithm has high robustness under label noise injection.
Table 19: Evaluation of dataset pruning algorithms on CIFAR-10 (ResNet-18) with 30% symmetric random label noise. Full dataset accuracy 83.06%.
Method
80%
87.5%
92%
96 %
Random
76.51
67.18
62.05
49.36
CCS
78.51
69.35
64.16
51.28
TDDS
77.44
67.99
63.66
50.85
Ours
82.26
73.44
68.34
59.17
Table shows that even when using substantially fewer epochs (c.g., 50 epochs = 9,750 iterations), our method still outperforms SOTA pruning methods trained for 40,000 iterations, demonstrating its strong performance robustness.
Table 20: Different number of training epochs on CIFAR-10 (ResNet-18) 18) under 1-bit color quanti- zation
Number of Epochs
Number of Training Iterations
Acc (%)
50
9,750
75.01
100
19,500
76.19
125
24,375
77.69
150
29,250
78.57
200
39,000
79.90
D.3 COMPUTATIONAL COMPLEXITY
Table 21 reports the computational complexity of DCQ, which scales linearly with the size of the dataset. Table 22 reports the time consumption of each stage in pipeline.
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Table 21: Dataset statisties including class count, image distribution, total size, and complexity.
Dataset
#Classes (C)
Images per Class (IPC) Total Images
Complexity
ImageNet-11K
1,000
1v120
~1.2M
(1,000 1200 x BackboneFLOOP))
ImageNet-21IK
21,000
=:70
144.7
(21 000 x 700 x Backbone[LLOPs)
Table 22: Time cost of generating different datasets with color quantization (sufficient memory).
Dataset
Palette- Learning Iitenton-Coopptattinn Differentiable Refinement Total Time
ImageNet-ID IK
108 mins
16 mins
30 mins
154 mins
HIFAR-10/100
20 seconds
seconds
30 seconds
52 seconds
E VISUALIZATION
Figure and Fand presents the visualizations of 1IFAR-10/100 and Tiny-ImageNet under different color quantization rates, respectively.
Original (24 Bits, 2 colors), CIFAR-10.
Original (24 Bits, 2 colors), CIFAR-100.
Bits (32 colors), CIFAR-10.
Bits (32 colors), CIFAR-100.
(c) Bit (2 colors), CIFAR-10.
(f) Bit colors), CIFAR-100.
Figure 7: Visualization of different datasets under different color quantization ratios. Left column: CIFAR-I0 images; Right column: CIFAR-IOW images. From top to bottom: original images, 5-bits quantization (32 colors), and 1-bit quantization (2 colors).
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Published as a conference paper at ICLR 2026
(a) Original (24 Bits, 2 colors), Tiny-ImageNet.
(b) 5 Bit (32 colors), Imag-ImageNet
(c) Bit (2 colors). Tiny-ImageNer
Figure 8: Visualization of Tiny-ImageNet under different color quantization ratios. From top to bottom: original images, 5-bits quantization (32 colors), and 1-bit quantization (2 colors).
Figure and 10 present the visualizations of CIFAR-10/100 and Tiny-ImageNet under different color quantization rates, respectively.
(a) 2 Bits (4 colors), CIFAR-10.
(c) 2 Bits (4 colors), CIFAR-100.
(b) Bits (8 colors), CIFAR-10.
(d) 3 Bits (8 colors), CIFAR-100.
Figure 9: Visualization of different datasets under different color quantization ratios. Left column: CIFAR-10 10 images; Right column: CIFAR- 100 images. From top to bottom: 2-bit guantization (4 colors), and 3-bit quantization (8 colors).
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Published as a conference paper at ICLR 2026
400ml
(a) Original (2 Bit (4 colors), Tiny-ImageNe,
Rechin
(b) Bit (8 colors), Tiny-ImageNet
Figure 10: Visualization of Siny-ImageNer under different color quantization ratios. From top to bottom: 2-bit quantization (4 colors) and 3-bit quantization (8 colors).
21


Reviews:
[{"review_id": "Edj8NIgQYW", "reviewer": "Reviewer_ivQG", "summary": "The paper proposes a novel method, Dataset Color Quantization (DCQ), that reduces dataset storage by compressing color information rather than discarding samples. Unlike traditional pruning or distillation methods that lower dataset size by removing images, DCQ targets color-space redundancy within images to achieve compression while preserving critical training information. The proposed method does not offer significant training or inference time savings. The experiments are based on classification tasks over CIFAR-10/100, TinyImageNet, and ImageNet-1k.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper's method is clearly stated, and the paper is easy to follow. \n\n2. The proposed method is intuitive and proven to be effective under the defined settings.", "weaknesses": "1. I am not very clear about the comparison with direct image quantization, especially as the size of the dataset increases. For example, if extending the experiment to ImageNet 22k, will the proposed method still outperform the direct image quantization?\n\n2. As the classification task is highly abstract, the proposed method could work. However, it is not clear if the method still works for dense prediction tasks such as image segmentation and object detection. It would make the paper stronger if there were some experiments on generative tasks. Besides, it is also interesting to see if the proposed method could work together with model quantization, which could lead to significant model training and inference speed-up.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5WhS3DB7Tc", "reviewer": "Reviewer_FTHp", "summary": "This paper proposes Dataset Color Quantization (DCQ), a framework for compressing visual datasets by reducing color-space redundancy while preserving information critical for model training. Unlike existing methods that prune samples or perform image-wise quantization, DCQ introduces shared palettes across chromatically similar images via clustering, guided by shallow-layer features of a pre-trained network. It further enhances semantic preservation through attention-guided bit allocation and optimizes palette quality using edge-preserving differentiable quantization. Experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K show that DCQ significantly outperforms both traditional color quantization and dataset pruning methods under aggressive compression.", "questions": "See weeknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-  This work proposes a dataset-level color quantization framework tailored specifically for training tasks, overcoming the limitations of traditional color quantization methods. It effectively balances the trade-off between storage compression and model trainability.\n\n-  This paper introduces the training-oriented dataset color quantization framework that integrates (1) a shared clustering-based palette, (2) attention-guided bit allocation, and (3) edge-preserving optimization. \n\n- The proposed method significantly outperforms existing color quantization and dataset pruning approaches across multiple benchmarks, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K. \n\n-  The experimental evaluation is comprehensive, covering multiple benchmark datasets and network architectures (e.g., ResNet, ShuffleNet, MobileNet-v2, ViT).", "weaknesses": "1. The method proposed in this paper reduces dataset storage by compressing the color space, whereas traditional dataset pruning achieves this goal by removing data samples. However, directly comparing these two approaches is somewhat unfair, as the method in this paper does not reduce the actual number of training samples. In other words, the proposed approach may not necessarily decrease the total number of training iterations required for the model training, leading to have some questions about the paper’s claim of improving model training efficiency. Therefore, the authors should analyze the relationship between the number of training iterations and accuracy.\n2. Some recent state-of-the-art or more related methods are missing, such as [1,2,3,4,5].\n3. Color quantization, in a sense, functions similarly to data augmentation. What if dataset pruning is also combined with a similar data augmentation strategy? If such a combination also yielded strong performance, the contribution of this paper would be somewhat diminished, because dataset pruning plus data augmentation would not only reduce storage requirements but also decrease the number of training iterations while maintaining performance.\n\n[1] Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty. ICML 2025.\n[2] Dataset Quantization. ICCV 2023\n[3] Dataset quantization with active learning based adaptive sampling. ECCV 2024.\n[4] Color-Oriented Redundancy Reduction in Dataset Distillation, NeurIPS 2024\n[5] Adaptive Dataset Quantization, AAAI2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WVnvikB2lL", "reviewer": "Reviewer_KUXK", "summary": "The paper introduces a training-oriented dataset color quantization framework that learns shared, attention-guided, texture-preserving color palettes at the dataset or cluster level to reduce storage while keeping the compressed dataset effective for training. The approach demonstrates strong classification accuracy under aggressive color-bit constraints and compares favorably to pruning and distillation methods at matched compression ratios.", "questions": "1. What is the end-to-end compute and memory overhead of the proposed DCQ pipeline compared with pruning or distillation methods at the same compression ratio, especially on large-scale datasets?\n\n2. How sensitive is the method to the choice of cluster count and attention mechanism, and is there a recommended default or auto-tuning strategy that can be applied across datasets and resolutions?\n\n3. How does the method behave on tasks where discriminative information is carried by subtle color patterns (e.g., fine-grained species classification)?\n\n4. Can the proposed palette sharing introduce harmful color aliasing or loss of spatial cues in detection/segmentation settings, and are there preliminary results in that direction?\n\n5. Does the attention-guided palette allocation amplify spurious or dataset-specific artifacts learned by the proxy model, and how robust is the method under label noise or distribution shift?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The work focuses on color-level redundancy as a distinct dimension of dataset compression, rather than only reducing samples or resolution. This makes the problem practically relevant for scenarios where storage and bandwidth are constrained but training quality must be preserved.\n\n2. The proposed framework combines chromaticity-aware clustering, attention-guided palette allocation, and differentiable, texture-preserving refinement. The components are mutually consistent and clearly aligned with the training objective.\n\n3. The method achieves strong results when colors are heavily quantized (e.g., 1–6 bits) and remains competitive against dataset pruning or distillation when the comparison is made at the same overall compression budget.\n\n4. Experiments on large-scale image classification settings indicate that the method can, in principle, be applied beyond toy scenarios.", "weaknesses": "1. Most individual elements (clustering, attention guidance, STE-based refinement) are known in the literature; the central contribution lies in integrating them for dataset-level color compression. Readers may perceive this as a strong system design rather than a conceptual breakthrough.\n\n2. The paper does not fully quantify the training-time overhead of palette learning, attention computation, and differentiable refinement,  particularly on large datasets and modern backbones.\n\n3. The evaluation is centered on image classification. It remains unclear how well the approach generalizes to tasks that are more sensitive to local or fine-grained color cues, such as fine-grained recognition, detection, or segmentation.\n\n4. While comparisons with pruning and distillation are useful, some aspects of the evaluation protocol (training schedules, augmentation ranges, and budget alignment) could be specified more tightly to eliminate possible confounding factors.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HY8kdDY0WI", "reviewer": "Reviewer_NGTe", "summary": "this paper investigates color quantization via the usage of network features and clustering methods. specifically, it does this from a perspective of training dataset compression, as opposed to the per-image compression for model inference in existing research [ColorCNN,  ColorCNN+]. first, it splits the images in a dataset into groups according to their neural network features, forming clusters of similar colors. then, for images within each cluster, it divides each image into the high and low importance regions according to Grad-CAM attention map. finally, the quantized color map is optimized through a differentiable palette optimization model. on several benchmarks, the authors reported competitive results.", "questions": "see above", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ using color quantization to compress training datasets seems an interesting direction.\n+ good presentation overall, with clear motivation and easy-to-read language. \n+ the bi-level compression method achieves good results, plus the visual results seem good.\n+ ablation study verifies the effectiveness of the proposed method.", "weaknesses": "- some clarity issues since there are two levels of k-means clustering. the reviewer recommends highlighting the cluster's level when introducing them, e.g., change the title for Table 2b and 2c.\n- please consider including more visual comparisons on different color spaces sizes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IUZG7yPpWb", "reviewer": "Reviewer_Jn5n", "summary": "The paper proposes Dataset Color Quantization (DCQ), a training-oriented framework that compresses entire training sets by reducing color-space redundancy while attempting to preserve semantic content and texture. The method involves three main stages:\n1. Chromaticity-Aware Clustering (CAC): Images are clustered based on their CNN features. A shared color palette is then generated for all images within the same cluster to enforce cross-image color consistency.\n2. Attention-Guided Palette Allocation: It uses Grad-CAM++ attention maps from a pre-trained model to identify semantically important regions. This guides the palette allocation to prioritize \"High-Impact Palettes\" crucial for model performance.\n3. Texture-Preserved Palette Optimization: Finally, it performs a differentiable palette refinement using a Sobel operator-based edge loss to minimize texture loss and preserve structural details.\n\nExperiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet-1K report strong accuracy at extremely low color bit-depths (e.g., 2-bit) and favorable comparisons against both image-wise Color Quantization (CQ) baselines and dataset-pruning methods", "questions": "See weakness. I wish the authors could clarify these questions, and I am willing to reevaluate the paper based on the reply.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The experimental gains are significant. For example, on CIFAR-10 at 2 bits (4 colors), DCQ achieves 89.15% accuracy, while the ColorCNN baseline (adapted for training) achieves 59.15%. The method also consistently outperforms all dataset pruning baselines at high compression ratios, such as a 79.9% accuracy on CIFAR-10 at a 96% pruning ratio (1-bit), far exceeding the next-best CCS method.\n\n2. The authors provide a thorough set of ablation studies to justify their design choices, including the optimal number of clusters (20), the superiority of shallow-layer features for clustering, the choice of attention mechanism (Grad-CAM++), the positive impact of the texture-preservation loss, and the optimal percentage of pixels to retain via attention (50%).\n\n3. The paper demonstrates that DCQ is orthogonal to other compression techniques like dataset pruning. It can be combined with pruning (e.g., CCSAUM) to achieve extreme compression ratios (up to 99.2%)  while maintaining high accuracy.", "weaknesses": "1. Baseline Fairness:\nCQ Baselines: The paper adapts several image-wise CQ methods (like ColorCNN) that were originally designed for inference (quantizing the test set) to a new training task (quantizing the train set). While the authors acknowledge this protocol difference, it's unclear if these baselines were properly tuned for this new \"quantize-then-train\" setting, which could overstate DCQ's relative performance.\n2.  The paper defines its compression ratio solely based on the reduction in color bits (e.g., $q_r = 1 - q/24$). This is optimistic as it only accounts for the per-pixel index map storage and ignores the significant overhead of storing the shared palettes (e.g., $3 \\times K \\times 2^q$ bits) and any metadata for cluster assignments. A true evaluation of compression would also require reporting the total on-disk file size (palettes + indices) and comparing it against standard codecs (like PNG, JPEG, or WebP) or learned image compressors at a similar quality level.\n3. The \"Attention-Guided Palette Allocation\" mechanism uses Grad-CAM++ generated from a \"task-specific pre-trained model\". This means information from the ground-truth labels (which trained the pre-trained model) is used to guide the compression of the training images. This is a form of information leakage that mixes supervision with the data representation itself, which could be a confounding factor in the results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)