Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria

• URL: http://arxiv.org/abs/2302.09560v1
• Date: Sun, 19 Feb 2023 12:38:20 GMT
• Title: Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria
• Authors: Hossam Amer, Sepideh Shaterian, and En-hui Yang
• Abstract summary: This paper presents Deep Selector-HV, an adaptive JPEG compression method that targets image classification. Deep Selector-HV selects adaptively a Quality Factor (QF) to compress the image so that a good trade-off between the Compression Ratio (CR) and classifier Accuracy (Accuracy performance) can be achieved.
• Score: 8.615661848178183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With limited storage/bandwidth resources, input images to Computer Vision (CV) applications that use Deep Neural Networks (DNNs) are often encoded with JPEG that is tailored to Human Vision (HV). This paper presents Deep Selector-JPEG, an adaptive JPEG compression method that targets image classification while satisfying HV criteria. For each image, Deep Selector-JPEG selects adaptively a Quality Factor (QF) to compress the image so that a good trade-off between the Compression Ratio (CR) and DNN classifier Accuracy (Rate-Accuracy performance) can be achieved over a set of images for a variety of DNN classifiers while the MS-SSIM of such compressed image is greater than a threshold value predetermined by HV with a high probability. Deep Selector-JPEG is designed via light-weighted or heavy-weighted selector architectures. Experimental results show that in comparison with JPEG at the same CR, Deep Selector-JPEG achieves better Rate-Accuracy performance over the ImageNet validation set for all tested DNN classifiers with gains in classification accuracy between 0.2% and 1% at the same CRs while satisfying HV constraints. Deep Selector-JPEG can also roughly provide the original classification accuracy at higher CRs.

Related papers

• JPEG Inspired Deep Learning [4.958744940097937]
  Well-crafted JPEG compression can actually improve the performance of deep learning (DL) We propose JPEG-DL, a novel DL framework that prepends any underlying DNN architecture with a trainable JPEG compression layer.
  arXiv  Detail & Related papers  (2024-10-09T17:23:54Z)
• JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients [17.437568540883106]
  We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. Our proposed network achieves state-of-the-art performance in flexible color image JPEG artifact removal tasks.
  arXiv  Detail & Related papers  (2024-04-03T03:28:04Z)
• Learned Lossless Compression for JPEG via Frequency-Domain Prediction [50.20577108662153]
  We propose a novel framework for learned lossless compression of JPEG images. To enable learning in the frequency domain, DCT coefficients are partitioned into groups to utilize implicit local redundancy. An autoencoder-like architecture is designed based on the weight-shared blocks to realize entropy modeling of grouped DCT coefficients.
  arXiv  Detail & Related papers  (2023-03-05T13:15:28Z)
• Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images [17.847661026367767]
  Visual Place Recognition (VPR) is a fundamental task that allows a robotic platform to successfully localise itself in the environment. JPEG is an image compression standard that can employ high compression ratios to facilitate lower data transmission for VPR applications. When applying high levels of JPEG compression, both the image clarity and size are drastically reduced.
  arXiv  Detail & Related papers  (2023-02-26T13:13:51Z)
• High-Perceptual Quality JPEG Decoding via Posterior Sampling [13.238373528922194]
  We propose a different paradigm for JPEG artifact correction. We aim to obtain sharp, detailed and visually reconstructed images, while being consistent with the compressed input. Our solution offers a diverse set of plausible and fast reconstructions for a given input with perfect consistency.
  arXiv  Detail & Related papers  (2022-11-21T19:47:59Z)
• Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder [73.48927855855219]
  We propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends. Experiments demonstrate that our approach successfully improves the rate-distortion performance over JPEG across various quality metrics.
  arXiv  Detail & Related papers  (2022-01-27T20:20:03Z)
• Towards Flexible Blind JPEG Artifacts Removal [73.46374658847675]
  We propose a flexible blind convolutional neural network, namely FBCNN, that can predict the adjustable quality factor to control the trade-off between artifacts removal and details preservation. Our proposed FBCNN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2021-09-29T17:12:10Z)
• Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge [52.880912557105106]
  JEC-RL is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning. It can achieve good security performance for JPEG images against both advanced feature based and modern CNN based steganalyzers.
  arXiv  Detail & Related papers  (2021-05-09T08:10:41Z)
• Learning to Improve Image Compression without Changing the Standard Decoder [100.32492297717056]
  We propose learning to improve the encoding performance with the standard decoder. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients. We do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder.
  arXiv  Detail & Related papers  (2020-09-27T19:24:42Z)
• Quantization Guided JPEG Artifact Correction [69.04777875711646]
  We develop a novel architecture for artifact correction using the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
  arXiv  Detail & Related papers  (2020-04-17T00:10:08Z)

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

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