Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification

• URL: http://arxiv.org/abs/2503.11954v1
• Date: Sat, 15 Mar 2025 01:52:09 GMT
• Title: Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification
• Authors: Ahcen Aliouat, Elsa Dupraz,
• Abstract summary: This paper investigates the use of low-density parity-check (LDPC) codes as an alternative entropy-coding approach.<n>It is hypothesized that the structured nature of LDPC codes can be leveraged more effectively by deep learning models for tasks like classification.<n>Experiments on datasets like MNIST, Fashion-MNIST, and CIFAR show that LDPC codes outperform Huffman and Arithmetic coding in classification tasks.
• Score: 3.2657732635702375
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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