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Frequency-Aware Transformer for Learned Image Compression at ICLR 2024


Core Concepts
Proposing a novel Frequency-Aware Transformer (FAT) block for learned image compression to capture multiscale and directional frequency components efficiently.
Abstract
The paper introduces the Frequency-Aware Transformer (FAT) block for learned image compression, addressing the limitations of existing methods in capturing anisotropic frequency components and preserving directional details. The FAT block comprises frequency-decomposition window attention (FDWA) modules and a frequency-modulation feed-forward network (FMFFN) to improve rate-distortion performance. Additionally, a transformer-based channel-wise autoregressive (T-CA) model is presented to exploit channel dependencies effectively. Experimental results show superior rate-distortion performance compared to existing methods. 1. Introduction Learned image compression models have emerged as effective solutions. Existing models use CNNs but have limitations in capturing anisotropic frequency components. Transformers are introduced to capture non-local spatial relationships for better R-D performance. 2. Methods Proposed a Frequency-Aware Transformer (FAT) block with FDWA and FMFFN. Introduced T-CA entropy model for modeling dependencies across frequency components. 3. Experiments Achieved state-of-the-art R-D performance on Kodak, Tecnick, and CLIC datasets. Outperformed VTM-12.1 by significant margins in BD-rate.
Stats
Experiments show outperformance of latest standardized codec VTM-12.1 by 14.5%, 15.1%, 13.0% in BD-rate on Kodak, Tecnick, and CLIC datasets.
Quotes
"Our method achieves state-of-the-art rate-distortion performance." "Evidently outperforms latest standardized codec VTM-12.1."

Key Insights Distilled From

by Han Li,Shaoh... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2310.16387.pdf
FTIC

Deeper Inquiries

How does the proposed FAT block address the limitations of existing LIC methods?

The Frequency-Aware Transformer (FAT) block addresses the limitations of existing Learned Image Compression (LIC) methods by introducing frequency-decomposition window attention (FDWA) modules. These FDWA modules capture multiscale and directional frequency components of natural images, allowing for more efficient latent representation. By utilizing diverse window shapes in parallel, the FAT block can extract different orientation and spatial frequency components simultaneously. This approach overcomes the limitation of traditional self-attention mechanisms that are inefficient in capturing directional frequency information due to their isotropic nature. Additionally, the FAT block incorporates a frequency-modulation feed-forward network (FMFFN) to adaptively modulate different frequency components. This modulation helps eliminate potential redundancy across various frequencies, leading to improved rate-distortion performance in image compression tasks. Overall, by integrating FDWA and FMFFN within the transformer architecture, the FAT block achieves state-of-the-art results compared to existing LIC models.

How can transformers be applied beyond image compression?

The introduction of transformers has significant implications for various fields beyond image compression: Natural Language Processing (NLP): Transformers have revolutionized NLP with models like BERT and GPT series. They excel at capturing long-range dependencies in sequential data, making them ideal for tasks such as language translation, sentiment analysis, and text generation. Computer Vision: Transformers have shown promise in computer vision tasks such as object detection, image classification, and segmentation. Models like Vision Transformers (ViTs) have demonstrated competitive performance on par with convolutional neural networks. Speech Recognition: Transformers are increasingly being used in speech recognition systems due to their ability to handle sequential data efficiently. They can capture complex patterns in audio signals and improve transcription accuracy. Recommendation Systems: Transformers are effective for building recommendation systems that analyze user behavior sequences or item interactions to provide personalized recommendations. Healthcare: In healthcare applications like medical imaging analysis or patient diagnosis prediction, transformers can process large volumes of complex data effectively while maintaining interpretability. Transformers' versatility makes them suitable for a wide range of applications across industries where processing sequential or structured data is essential.
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