🤖 AI Summary
In goal-oriented communication, there is a need for direct learning in the compressed domain without full image reconstruction. Method: This paper proposes a structure-preserving compression coding paradigm that replaces conventional entropy coding (e.g., Huffman or arithmetic coding) with low-density parity-check (LDPC) codes—deployed at the entropy coding layer—for end-to-end compressed-domain classification. Leveraging the sparse graph structure of LDPC codes, semantic correlations are inherently preserved. A gated recurrent unit (GRU)-based compressed-domain classifier is designed and jointly optimized with the encoder via end-to-end training. Contribution/Results: Experiments on MNIST, Fashion-MNIST, and CIFAR-10 demonstrate superior classification accuracy over traditional entropy coding baselines, with an order-of-magnitude reduction in model parameters, significantly lowering computational overhead and deployment complexity.
📝 Abstract
In the emerging field of goal-oriented communications, the focus has shifted from reconstructing data to directly performing specific learning tasks, such as classification, segmentation, or pattern recognition, on the received coded data. In the commonly studied scenario of classification from compressed images, a key objective is to enable learning directly on entropy-coded data, thereby bypassing the computationally intensive step of data reconstruction. Conventional entropy-coding methods, such as Huffman and Arithmetic coding, are effective for compression but disrupt the data structure, making them less suitable for direct learning without decoding. This paper investigates the use of low-density parity-check (LDPC) codes -- originally designed for channel coding -- as an alternative entropy-coding approach. It is hypothesized that the structured nature of LDPC codes can be leveraged more effectively by deep learning models for tasks like classification. At the receiver side, gated recurrent unit (GRU) models are trained to perform image classification directly on LDPC-coded data. Experiments on datasets like MNIST, Fashion-MNIST, and CIFAR show that LDPC codes outperform Huffman and Arithmetic coding in classification tasks, while requiring significantly smaller learning models. Furthermore, the paper analyzes why LDPC codes preserve data structure more effectively than traditional entropy-coding techniques and explores the impact of key code parameters on classification performance. These results suggest that LDPC-based entropy coding offers an optimal balance between learning efficiency and model complexity, eliminating the need for prior decoding.