Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes

Основные понятия

Direct learning over LDPC-coded images enhances classification efficiency without decoding.

Аннотация

In the context of goal-oriented communications, this study explores the efficiency of applying Deep Learning models directly to compressed data without prior decoding. The authors propose using Low-Density Parity Check (LDPC) codes for entropic coding, aiming to leverage the internal code structure for better exploitation by Deep Learning models. By employing Recurrent Neural Networks (RNNs), specifically Gated Recurrent Unit (GRU), for image classification based on LDPC-coded bit-planes, the study demonstrates superior classification accuracy compared to traditional entropic coding methods like Huffman and Arithmetic. The approach eliminates the need for any form of decompression before applying the learning model, showcasing a novel perspective on compression techniques and Deep Learning synergy.

Статистика

Our numerical results indicate that classification based on LDPC-coded bit-planes surpasses Huffman and Arithmetic coding.
The LDPC parity-check matrix used has a rate of 1/2.
The proposed GRU model requires only 19k learnable parameters to outperform other models.
By encoding one bit-plane (MSB), the compression ratio can reach up to 0.5 bits per pixel.

Цитаты

"Direct learning over compressed data, without any prior decoding, holds promise for enhancing time-efficient execution of inference models."
"Our experimental results demonstrate the feasibility of applying image classification directly over LDPC-coded syndromes without partial decoding."
"This approach offers a new perspective on the synergy between compression techniques and Deep Learning models."

Ключевые выводы из

Learning on JPEG-LDPC Compressed Images

by Ahcen Alioua... в arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10202.pdf

Дополнительные вопросы

How might different LDPC codes impact learning performance in this context

In the context of learning on LDPC-coded images, different LDPC codes can have varying impacts on learning performance. The choice of LDPC code parameters such as the parity-check matrix size, rate, and structure can influence how well the Deep Learning model leverages the encoded data for classification. For instance, using a regular (3, 6)-LDPC parity-check matrix with a specific rate may provide better preservation of image features and structures during compression. Irregular LDPC codes could offer more flexibility in coding certain types of images or datasets that exhibit unique characteristics. Additionally, optimizing LDPC codes specifically for learning from syndromes could lead to enhanced performance by tailoring the code's properties to suit the requirements of the classification task.

What are potential drawbacks or limitations of directly applying Deep Learning models to compressed data

Directly applying Deep Learning models to compressed data poses several potential drawbacks and limitations. One major limitation is the loss of information or distortion introduced during compression processes like quantization or encoding with entropy coders such as Huffman or Arithmetic coding. This lossy transformation can impact classification accuracy by altering pixel values and spatial relationships crucial for image understanding tasks. Furthermore, working directly on compressed data without decoding may restrict access to detailed information necessary for complex inference tasks that require fine-grained features present in original uncompressed images. The lack of reconstruction capability also limits applications where precise pixel-level details are essential.

How could pruning strategies affect both compression gain and accuracy in this framework

Pruning strategies in this framework could significantly affect both compression gain and accuracy outcomes. By selectively removing less critical syndromes or bit-planes before LDPC coding, pruning can enhance compression efficiency by reducing redundant information while maintaining essential features for classification tasks intact within fewer bits per pixel representation. However, aggressive pruning might lead to a trade-off between higher compression ratios and reduced accuracy due to discarding potentially valuable data points needed for accurate classification decisions. Balancing pruning levels based on importance metrics derived from training dynamics or feature relevance analysis could optimize both compression gains and model performance simultaneously.