Learned Image Coding for Machines: A Content-Adaptive Approach

• URL: http://arxiv.org/abs/2108.09992v1
• Date: Mon, 23 Aug 2021 07:53:35 GMT
• Title: Learned Image Coding for Machines: A Content-Adaptive Approach
• Authors: Nam Le, Honglei Zhang, Francesco Cricri, Ramin Ghaznavi-Youvalari, Hamed Rezazadegan Tavakoli, Esa Rahtu
• Abstract summary: Machine-to-machine communication represents a new challenge and opens up new perspectives in the context of data compression. We present an inference-time content-adaptive finetuning scheme that optimize the latent representation of an end-to-end learned image. Our system achieves -30.54% BD-rate over the state-of-the-art image/video Coding (VVC)
• Score: 24.749491401730065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-machine communication of images and videos represents a new challenge and opens up new perspectives in the context of data compression. One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption. Another approach consists of developing completely new compression paradigms and architectures for machine-to-machine communications. In this paper, we focus on image compression and present an inference-time content-adaptive finetuning scheme that optimizes the latent representation of an end-to-end learned image codec, aimed at improving the compression efficiency for machine-consumption. The conducted experiments show that our online finetuning brings an average bitrate saving (BD-rate) of -3.66% with respect to our pretrained image codec. In particular, at low bitrate points, our proposed method results in a significant bitrate saving of -9.85%. Overall, our pretrained-and-then-finetuned system achieves -30.54% BD-rate over the state-of-the-art image/video codec Versatile Video Coding (VVC).

Related papers

• Accelerating Learned Video Compression via Low-Resolution Representation Learning [18.399027308582596]
  We introduce an efficiency-optimized framework for learned video compression that focuses on low-resolution representation learning. Our method achieves performance levels on par with the low-decay P configuration of the H.266 reference software VTM.
  arXiv  Detail & Related papers  (2024-07-23T12:02:57Z)
• Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression [60.689646881479064]
  We propose a progressive learning paradigm for transformer-based variable-rate image compression. Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively. Our model outperforms all current variable image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed image compression methods trained from scratch.
  arXiv  Detail & Related papers  (2023-11-23T08:29:32Z)
• Device Interoperability for Learned Image Compression with Weights and Activations Quantization [1.373801677008598]
  We present a method to solve the device interoperability problem of a state-of-the-art image compression network. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly.
  arXiv  Detail & Related papers  (2022-12-02T17:45:29Z)
• Preprocessing Enhanced Image Compression for Machine Vision [14.895698385236937]
  We propose a preprocessing enhanced image compression method for machine vision tasks. Our framework is built upon the traditional non-differential codecs. Experimental results show our method achieves a better tradeoff between the coding and the performance of the downstream machine vision tasks by saving about 20%.
  arXiv  Detail & Related papers  (2022-06-12T03:36:38Z)
• Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement [74.1052624663082]
  We develop a deep learning architecture capable of restoring detail to compressed videos. We show that this improves restoration accuracy compared to prior compression correction methods. We condition our model on quantization data which is readily available in the bitstream.
  arXiv  Detail & Related papers  (2022-01-31T18:56:04Z)
• 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)
• Image coding for machines: an end-to-end learned approach [23.92748892163087]
  In this paper, we propose an image for machines which is neural network (NN) based and end-to-end learned. Our results show that our NN-based task outperforms the state-of-the-art Versa-tile Video Coding (VVC) standard on the object detection and instance segmentation tasks. To the best of our knowledge, this is the first end-to-end learned machine-targeted image distortion.
  arXiv  Detail & Related papers  (2021-08-23T07:54:42Z)
• Conditional Entropy Coding for Efficient Video Compression [82.35389813794372]
  We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% savings without trading off decoding speed.
  arXiv  Detail & Related papers  (2020-08-20T20:01:59Z)
• Content Adaptive and Error Propagation Aware Deep Video Compression [110.31693187153084]
  We propose a content adaptive and error propagation aware video compression system. Our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system.
  arXiv  Detail & Related papers  (2020-03-25T09:04:24Z)
• A Unified End-to-End Framework for Efficient Deep Image Compression [35.156677716140635]
  We propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies. Specifically, we design an auto-encoder style network for learning based image compression. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
  arXiv  Detail & Related papers  (2020-02-09T14:21:08Z)
• An Emerging Coding Paradigm VCM: A Scalable Coding Approach Beyond Feature and Signal [99.49099501559652]
  Video Coding for Machine (VCM) aims to bridge the gap between visual feature compression and classical video coding. We employ a conditional deep generation network to reconstruct video frames with the guidance of learned motion pattern. By learning to extract sparse motion pattern via a predictive model, the network elegantly leverages the feature representation to generate the appearance of to-be-coded frames.
  arXiv  Detail & Related papers  (2020-01-09T14:18:18Z)

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.