Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications

Lahiru D. Chamain; Zhi Ding

REVIEW 3 major objections 5 minor 16 references

Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read JPEG2000 images classify directly from wavelet coefficients, skipping reconstruction.

desk verdict A useful compressed-domain classification paper that delivers real speed savings and a novel DWT augmentation, but the augmentation's exactness claim doesn't survive JPEG2000 quantization. read the letter →

arxiv 1909.05638 v1 pith:AUJJ3ZBQ submitted 2019-09-04 cs.CV cs.LGcs.MMeess.IVstat.ML

Lahiru D. Chamain , Zhi Ding This is my paper · ORCID

classification cs.CVcs.LGcs.MMeess.IVstat.ML

keywords JPEG2000CDF9/7waveletDWT-domainclassificationcompressed-domaininferenceimageaugmentationcloudbandwidth-limitednetworksResNet

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that a deep CNN can classify JPEG2000-compressed images directly from the CDF 9/7 wavelet coefficients already present in the compressed stream, so the inverse wavelet transform, color conversion, and level offset are unnecessary. On CIFAR-10 and Tiny ImageNet, it reports that this compressed-domain approach matches or slightly beats RGB-domain classification while using shallower networks, and that it removes more than 80 percent of decoder reconstruction work. The accuracy gain rests on a new augmentation recipe: rather than flipping or shifting DWT coefficients directly, the spatial flip or shift is conjugated by the wavelet matrix, preserving the geometric meaning of the operation. If the paper is right, JPEG2000 classification can be both faster and more accurate in networked, bandwidth-limited settings without changing the codec.

What carries the argument

The key object is the matrix $A$ expressing the level-1 CDF 9/7 DWT as a separable linear transform, factored through the codec's lifting steps as $A = P_1 U_1 P_2 U_2 S$ (predictions, updates, and de-interleaving). Because $A$ is invertible, a spatial augmentation $H$ is replaced in the DWT domain by the conjugated transform $H' = A^{-1} H A$, which is what makes flip and shift augmentation meaningful for compressed-domain inputs. The same matrix representation also lets the authors compute DWT coefficients quickly during training, avoiding a slow wavelet pass over each image.

What would settle it

Train a model on DWT coefficients produced by the similarity transform of heavily quantized JPEG2000 coefficients, and a second model on coefficients obtained by compressing spatially flipped or shifted originals at the same bitrate; if accuracy of the first falls behind the second as the quantization step grows, the exact-equivalence claim fails for lossy streams.

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Extended reading notes

Core claim

The central claim is that dequantized CDF 9/7 DWT coefficients taken from inside a JPEG2000 decoder are a sufficient, and with the right augmentation a better, input representation for CNN classification than reconstructed RGB images. Representing the level-1 DWT as an invertible matrix $A$, the authors define augmentation by the similarity transform $H' = A^{-1} H A$, so that a horizontal flip or shift of the DWT coefficients corresponds to the same geometric operation on the original image. On CIFAR-10 this yields 91.92 percent accuracy for a 21-convolution-layer DWT ResNet versus 91.70 percent for a 27-layer RGB ResNet, and Tiny ImageNet shows a smaller advantage; both use fewer layers in the DWT domain. The practical message is that the decoder can stop after dequantization and the harvested coefficients train models that generalize across compression ratios.

Load-bearing premise

The load-bearing premise is that the codec's wavelet transform is a perfect linear operator captured by the matrix $A$, so that flipping or shifting DWT coefficients via $A^{-1} H A$ produces exactly the coefficients of a flipped or shifted original image; JPEG2000's lossy quantization breaks that exact equivalence.

Editorial extensions

If this is right

A JPEG2000 decoder can hand the dequantized DWT coefficients directly to a classifier, eliminating the inverse DWT and color conversion; the reported reconstruction saving is over 80 percent of decode time and grows with image size.
Shallower ResNets suffice in the DWT domain, with CIFAR-10 using 21 convolution layers instead of 27 and Tiny ImageNet using 31 instead of 40, matching or improving accuracy while speeding up training and inference.
The proposed DWT-domain augmentation recovers more than two percentage points of top-1 accuracy on CIFAR-10 compared with naive augmentation of DWT coefficients, making compressed-domain training viable.
A model trained on uncompressed coefficients can be fine-tuned for lower-bandwidth channels, cutting training time by about 75 percent while improving accuracy, and the DWT-domain accuracy advantage persists as bandwidth shrinks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the same coefficient-harvesting trick ports to other transform codecs, any decoder that exposes quantized coefficients could feed a classifier before reconstruction, thinning edge-to-cloud inference pipelines.
The augmentation identity assumes the encoder's DWT is exactly the linear operator $A$; under heavy JPEG2000 quantization the augmented coefficients will not exactly equal coefficients of a spatially augmented original, so a quantization-aware version of $H'$ is the natural next test.
Because the inputs are level-1 subbands, the same pipeline could support progressive or multi-resolution classification, using only the low-frequency subband for a quick decision and adding detail subbands when bandwidth allows; this goes beyond what the paper tests.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a genuinely useful paper. It's the first to show you can classify straight from CDF 9/7 coefficients harvested inside a standard JPEG2000 decoder, and the reported time savings from skipping reconstruction are real. The main soft spot is the augmentation theory: the similarity transform H' = A^{-1}HA is exact only if the DWT is lossless, but the actual inputs are deadzone-quantized and dequantized coefficients. Figure 3 only demonstrates exactness on an unquantized image. That's a genuine gap, but I don't think it sinks the paper. The augmentation clearly helps empirically (about 2.5 points on CIFAR-10, 1.5 on Tiny ImageNet), and even if it isn't precisely equivalent to spatial augmentation, it's evidently a good DWT-domain regularizer. The claim that it 'exactly reproduces' spatial transforms is overstrong; the authors should either condition it on no quantization or quantify the mismatch.

What's actually new: taking CDF 9/7 coefficients from inside the JPEG2000 decoder and feeding them to a ResNet, plus the proposed augmentation transformations. Prior work used DCT or unquantized DWT; this is the first to work with the actual lossy-j2k coefficients. The reconstruction gain numbers (80%+ of decode time) are meaningful and grow with image size. Experiments are competently run on two datasets, with standard deviations over three runs, and the speed/accuracy trade-off curves are informative.

Soft spots: the accuracy gains over RGB are tiny (0.2–0.3 pp), so the pitch should be speed and bandwidth, not accuracy. 'Shallower' is a bit loose: the DWT models use fewer conv layers but actually have more parameters (Table 2), though the smaller spatial resolution keeps them faster. Code: only the augmentation matrix code is linked; the full training pipeline isn't, so the central numbers aren't fully reproducible from the paper alone. The self-citation to Quannet is fine; it's directly relevant and not excessive.

The quantization-mismatch concern is real and load-bearing for the theoretical justification, but the paper's main claims — faster inference and comparable accuracy — hold up. The augmentation improves accuracy even if its exact-equivalence story is approximate. I'd send this to peer review and ask the authors to address the quantization effect, release more code, and tone down the exactness wording. For anyone working on compressed-domain vision, this is worth reading.

Referee Report

3 major / 5 minor

Summary. The paper addresses cloud-based image classification of JPEG2000-compressed images. The authors propose bypassing the RGB reconstruction step by feeding the CDF 9/7 DWT coefficients, extracted from inside the JPEG2000 decoder after dequantization, directly to a CNN classifier. They introduce a DWT-domain augmentation rule based on a similarity transform H' = A^{-1}HA (Eq. 3), claim that conventional spatial augmentations are ineffective in the DWT domain, and demonstrate on CIFAR-10 and Tiny ImageNet that shallower DWT-domain models can match or slightly exceed RGB-domain accuracy while saving more than 80% of decoding time. They also study performance under varying JPEG2000 compression ratios and show that fine-tuning from a pretrained model saves training time.

Significance. If the central claims hold, the work is practically valuable: skipping inverse DWT and color conversion gives a large, image-size-dependent decoding speedup, and the proposed augmentation would be a useful tool for training classifiers directly on JPEG2000 coefficients. The paper's strengths are its clear system motivation, the explicit reconstruction-gain measurement (Table 1), and the fact that the DWT inputs are obtained from a real codec (OpenJPEG) rather than from synthetic clean coefficients. The main theoretical contribution, Eq. (3), is clean for an exactly invertible linear transform, but its validity in the actual lossy JPEG2000 operating regime is not established, which is the key risk to the claimed accuracy improvements. The empirical speed benefits are credible; the accuracy improvements over RGB are small and not always statistically separated.

major comments (3)

[Section 3.3, Eq. (3), Figure 3] The proposed augmentation H' = A^{-1}HA assumes that the DWT is an exactly invertible linear operator and that the network input XwA is the exact DWT of the spatial image Xs. In the actual JPEG2000 pipeline, however, the classifier inputs are dequantized coefficients; the deadzone quantizer is nonlinear and non-invertible. Applying H' to dequantized coefficients will generally not produce the same coefficients that would be obtained by compressing the spatially augmented RGB image at the same rate. The paper validates the 'exactly same' claim only on an unquantized image (Figure 3) and provides no analysis or experiment quantifying the mismatch under lossy compression (r > 0). Since the proposed augmentation is the main source of the accuracy gain over plain DWT inputs (about 2.5% on CIFAR-10 and 1.5% on Tiny ImageNet), the central contribution is unverified in its operating regime. The authors should either prove that the quantization error is small under the augmentation, or empirically compare the proposed augmented training against training with exactly matched compressed-augmented samples across compression ratios.
[Section 4.2, Tables 3 and 4] The claim that the proposed method achieves 'better accuracy' than RGB-domain classification is not statistically well supported. On CIFAR-10 the difference is 91.92% vs 91.70% with standard deviations of ±0.11 and ±0.07, and on Tiny ImageNet top-1 it is 67.56% vs 67.35% with ±0.09 and ±0.11. In both cases the intervals overlap and the differences are on the order of 1-2 combined standard deviations. The robust and consistent improvements are between CDF 9/7Aug and plain CDF 9/7, not between DWT and RGB. The abstract and conclusion should be revised to state the comparison more precisely, or the authors should provide a significance test or more runs.
[Section 4.2 and Table 1] Several experimental details required for reproducibility are missing. The paper does not specify the exact JPEG2000 compression ratio r used for each accuracy table, the number of DWT decomposition levels, whether the level offset of 128 is applied before extracting coefficients, or how the 12 DWT subbands are scaled/normalized before being fed to the CNN. It is also unclear whether the reported training and inference speeds include the cost of the DWT extraction and the augmentation transform, or only the CNN forward/backward pass. These details are necessary to reproduce the claimed speedups and accuracy numbers, and should be stated explicitly.

minor comments (5)

[Section 3.3, Eq. (2)] The equation 'XsH = XsH' appears to be a typo; it should be written as a matrix product, e.g., XsH = Xs·H, for the subsequent derivation of Eq. (3) to be meaningful.
[Abstract and Figure 3 caption] There are several typographical errors: 'spatial RBG domain' should be 'RGB', and the Figure 3 caption contains 'ﬁllped' instead of 'flipped'.
[Figure 3] The caption states that the image 'Brownie' is of size 112×112, but no context is given for how this image relates to the CIFAR-10 or Tiny ImageNet datasets used in the experiments.
[Section 2, reference [5]] Reference [5] is a self-citation of the authors' prior work on DB1 wavelet coefficients; the paper should more clearly delineate what is new in the present work beyond changing the wavelet basis, especially because the augmentation method is the stated novelty.
[Section 3.7, footnote 2] The code for the DWT matrix transformation is shared via a Google Drive link, which is not a stable archival repository; the authors should include the code as supplementary material or in a public repository with a persistent DOI.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the DWT-domain augmentation is derived from linear DWT algebra, and the sole self-citation is not load-bearing.

full rationale

The paper's central claim is that classification can be performed directly on JPEG2000 CDF 9/7 DWT coefficients, with a specialized augmentation transform H' = A^{-1} H A in Eq. (3). This is not circular: it follows algebraically from the linear, invertible DWT representation XwA = A^T Xs A in Eq. (1), and the paper demonstrates the equivalence on an unquantized image in Figure 3. No parameter is fitted to the reported accuracy numbers; the DWT inputs are harvested from the external OpenJPEG codec and compared with independently trained RGB-domain baselines on CIFAR-10 and Tiny ImageNet. The only self-citation is to the authors' prior work [5], used for the deadzone quantizer description, a bandwidth-behavior comparison, and an unquantized CDF 9/7 accuracy baseline; none of these carry the paper's main claim. The concern that JPEG2000 deadzone quantization breaks the exact identity between H' applied to dequantized coefficients and the DWT of a spatially augmented compressed image is a real correctness and empirical-validation issue, but it is not circularity: the claimed identity is derived from the linear-algebra definitions rather than assumed from the target accuracy results. Therefore no load-bearing step reduces by construction to its own input.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters are fitted to make the central result hold; the DWT matrix is derived from the standard lifting scheme. The key assumptions are about the linearity and invertibility of the codec's DWT and the raw usability of quantized coefficients as CNN inputs.

assumptions (3)

domain assumption The 2D DWT is a linear invertible operation expressible as Xw = A^T Xs A, with A computed from the lifting implementation.
Used in Eq. (1) and Section 3.7 to derive H' = A^{-1} H A. The paper does not provide the exact values of A or verify that OpenJPEG's boundary handling matches this matrix model.
domain assumption Dequantized CDF 9/7 coefficients from the JPEG2000 decoder are directly usable as CNN inputs without normalization or careful scaling of subbands.
The paper feeds the coefficients into ResNet as if they were image channels; no input normalization or subband-wise scaling is described in Section 3.2.
domain assumption JPEG2000 irreversible compression uses the default color transform and no chroma subsampling, so the stacked level-1 DWT subbands of Y, Cb, Cr are all at half resolution.
Section 3.2 stacks 12 subbands at half resolution; this assumes the codec does not downsample chroma components.

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Cite this review

Pith. "Pith review of Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications." pith.science (2026). https://pith.science/paper/AUJJ3ZBQ

@misc{pith2026190905638,
  author       = {Pith},
  title        = {Pith review of: Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AUJJ3ZBQ}},
  note         = {Machine review of arXiv:1909.05638}
}

read the original abstract

JPEG2000 (j2k) is a highly popular format for image and video compression.With the rapidly growing applications of cloud based image classification, most existing j2k-compatible schemes would stream compressed color images from the source before reconstruction at the processing center as inputs to deep CNNs. We propose to remove the computationally costly reconstruction step by training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients directly extracted from j2k-compressed images. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy in the DWT domain. Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost. This way, faster and more accurate classification is possible for j2k encoded images without image reconstruction. Through experiments on CIFAR-10 and Tiny ImageNet data sets, we show that the performance of the proposed solution is consistent for image transmission over limited channel bandwidth.

Figures

Figures reproduced from arXiv: 1909.05638 by the authors.

**Figure 2.** (a). Test accuracy vs inference speed for the CIFAR-10 data set. The blue lines represent [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** (a) Original image. ‘Brownie’ of size 112 × 112. (b) shows the horizontally flipped image in RGB domain. But at the training, mini batches are in DWT domain. (c) shows the result of using the same transformation in DWT domain and reconstructed in RGB for visualization. (d) horizontally flipped in DWT domain with proposed augmentation transform and reconstructed in RGB for visualization. (b) and (d) are exactly same … view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: ResNet(w, nc, ns) represents a ResNet with ns = 3 stacks, each stack S(nb, w) consists of nb identical blocks for input image size w and nc channels. Each block B(nfs) consists of 2 convolution layers with nfs filters for the residual path and an identity connection fo…

**Figure 5.** Figure 5: (a). Models designed for CDF 9/7 DWT coefficients using the proposed augmentations [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Fine tuning for r = 5, 10, 15 using the pretrained DWT model for r = 0 reduces training time by 75% and improves accuracy. This observation is consistent with pre-training in RGB domain. CDF and RGB inputs use model ‘b’ and ‘e’ respectively. 5 Conclusions This work inv…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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L. Gueguen, A. Sergeev, B. Kadlec, R. Liu, and J. Yosinski, “Faster neural networks straight from jpeg,” in Advances in Neural Information Processing Systems , 2018, pp. 3937–3948

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X. Zou, X. Xu, C. Qing, and X. Xing, “High speed deep networks based on discrete cosine transformation,” in IEEE Int. Conf. on Image Processing (ICIP) , Oct 2014, pp. 5921–5925

work page 2014

[3] [3]

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T. Williams and R. Li, “Advanced image classiﬁcation using wavelets and convolutional neural networks,” in 15th IEEE Int. Conf. on Machine Learning and Applications (ICMLA) , Dec 2016, pp. 233–239

work page 2016

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D. Fu and G. Guimaraes, “Using compression to speed up image classiﬁcation in artiﬁcial neural networks,” 2016

work page 2016

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Quannet: Joint image compression and classiﬁcation over the channels with limited bandwidth,

L.D. Chamain, Z. Ding, and S.S. Cheung, “Quannet: Joint image compression and classiﬁcation over the channels with limited bandwidth,” IEEE Inter . Conf. on Multimedia and Expo, 2019, in press

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F. Jiang, W. Tao, S. Liu, J. Ren, X. Guo, and D. Zhao, “An end-to-end compression framework based on convolutional neural networks,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 3007–3018, 2018

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