The Lossy Horizon: Error-Bounded Predictive Coding for Lossy Text Compression (Episode I)
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918171234009088 |
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| author | Aghanya, Nnamdi Li, Jun Wang, Kewei |
| author_facet | Aghanya, Nnamdi Li, Jun Wang, Kewei |
| contents | Large Language Models (LLMs) can achieve near-optimal lossless compression by acting as powerful probability models. We investigate their use in the lossy domain, where reconstruction fidelity is traded for higher compression ratios. This paper introduces Error-Bounded Predictive Coding (EPC), a lossy text codec that leverages a Masked Language Model (MLM) as a decompressor. Instead of storing a subset of original tokens, EPC allows the model to predict masked content and stores minimal, rank-based corrections only when the model's top prediction is incorrect. This creates a residual channel that offers continuous rate-distortion control. We compare EPC to a simpler Predictive Masking (PM) baseline and a transform-based Vector Quantisation with a Residual Patch (VQ+RE) approach. Through an evaluation that includes precise bit accounting and rate-distortion analysis, we demonstrate that EPC consistently dominates PM, offering superior fidelity at a significantly lower bit rate by more efficiently utilising the model's intrinsic knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Lossy Horizon: Error-Bounded Predictive Coding for Lossy Text Compression (Episode I) Aghanya, Nnamdi Li, Jun Wang, Kewei Machine Learning Computation and Language Information Theory 94A08, 68P30, 68T50 E.4; I.2.7; I.2.7 Large Language Models (LLMs) can achieve near-optimal lossless compression by acting as powerful probability models. We investigate their use in the lossy domain, where reconstruction fidelity is traded for higher compression ratios. This paper introduces Error-Bounded Predictive Coding (EPC), a lossy text codec that leverages a Masked Language Model (MLM) as a decompressor. Instead of storing a subset of original tokens, EPC allows the model to predict masked content and stores minimal, rank-based corrections only when the model's top prediction is incorrect. This creates a residual channel that offers continuous rate-distortion control. We compare EPC to a simpler Predictive Masking (PM) baseline and a transform-based Vector Quantisation with a Residual Patch (VQ+RE) approach. Through an evaluation that includes precise bit accounting and rate-distortion analysis, we demonstrate that EPC consistently dominates PM, offering superior fidelity at a significantly lower bit rate by more efficiently utilising the model's intrinsic knowledge. |
| title | The Lossy Horizon: Error-Bounded Predictive Coding for Lossy Text Compression (Episode I) |
| topic | Machine Learning Computation and Language Information Theory 94A08, 68P30, 68T50 E.4; I.2.7; I.2.7 |
| url | https://arxiv.org/abs/2510.22207 |