The explosive growth of multi-source multimedia data has significantly increased the demands for transmission and storage, placing substantial pressure on bandwidth and storage infrastructures. While Autoregressive Compression Models (ACMs) have markedly improved compression efficiency through probabilistic prediction, current approaches remain constrained by two critical limitations: suboptimal compression ratios due to insufficient fine-grained feature extraction during probability modeling, and real-time processing bottlenecks caused by high resource consumption and low compression speeds. To address these challenges, we propose Efficient Dual-path Parallel Compression (EDPC), a hierarchically optimized compression framework that synergistically enhances modeling capability and execution efficiency via coordinated dual-path operations. At the modeling level, we introduce the Information Flow Refinement (IFR) metric grounded in mutual information theory, and design a Multi-path Byte Refinement Block (MBRB) to strengthen cross-byte dependency modeling via heterogeneous feature propagation. At the system level, we develop a Latent Transformation Engine (LTE) for compact high-dimensional feature representation and a Decoupled Pipeline Compression Architecture (DPCA) to eliminate encoding-decoding latency through pipelined parallelization. Experimental results demonstrate that EDPC achieves comprehensive improvements over state-of-the-art methods, including a 2.7x faster compression speed, and a 3.2% higher compression ratio. These advancements establish EDPC as an efficient solution for real-time processing of large-scale multimedia data in bandwidth-constrained scenarios. Our code is available at https://github.com/Magie0/EDPC.

翻译：多源多媒体数据的爆炸式增长显著增加了传输与存储需求，对带宽与存储基础设施构成了巨大压力。尽管自回归压缩模型通过概率预测显著提升了压缩效率，现有方法仍受限于两个关键瓶颈：概率建模过程中细粒度特征提取不足导致的次优压缩比，以及高资源消耗与低压缩速度引发的实时处理瓶颈。为应对这些挑战，我们提出高效双路并行压缩框架，该层次化优化的压缩框架通过协调的双路操作协同增强建模能力与执行效率。在建模层面，我们引入基于互信息理论的信息流细化度量，并设计多路径字节细化模块，通过异构特征传播强化跨字节依赖建模。在系统层面，我们开发了用于紧凑高维特征表示的潜在变换引擎，以及通过流水线并行化消除编解码延迟的解耦流水线压缩架构。实验结果表明，EDPC相较于现有最优方法实现了全面改进：压缩速度提升2.7倍，压缩比提高3.2%。这些进展使EDPC成为带宽受限场景下大规模多媒体数据实时处理的高效解决方案。代码已开源：https://github.com/Magie0/EDPC。