  In goal-oriented communications, the objective of the receiver is often to
apply a Deep-Learning model, rather than reconstructing the original data. In
this context, direct learning over compressed data, without any prior decoding,
holds promise for enhancing the time-efficient execution of inference models at
the receiver. However, conventional entropic-coding methods like Huffman and
Arithmetic break data structure, rendering them unsuitable for learning without
decoding. In this paper, we propose an alternative approach in which entropic
coding is realized with Low-Density Parity Check (LDPC) codes. We hypothesize
that Deep Learning models can more effectively exploit the internal code
structure of LDPC codes. At the receiver, we leverage a specific class of
Recurrent Neural Networks (RNNs), specifically Gated Recurrent Unit (GRU),
trained for image classification. Our numerical results indicate that
classification based on LDPC-coded bit-planes surpasses Huffman and Arithmetic
coding, while necessitating a significantly smaller learning model. This
demonstrates the efficiency of classification directly from LDPC-coded data,
eliminating the need for any form of decompression, even partial, prior to
applying the learning model.
