N - O Cool - chic：实现神经图像压缩中快速编码与轻量级解码的协调
N-O Cool-chic: reconcile fast encoding with lightweight decoding for neural image compression
AI总结：
研究针对过度拟合图像编解码器编码时间长的问题，提出绕过过度拟合过程，用编码器网络补充解码器的方法，降低Cool - chic编码复杂度，所提N - O Cool - chic编码复杂度降1000倍且性能具竞争力。
AI中文摘要：
过度拟合的图像编解码器通过为每个图像学习轻量级解码器来实现强大的压缩性能和低解码器复杂度，如Cool - chic，其图像编码性能与VVC相当，但每个解码像素需要约2000次乘法。然而，其编码时间长，不利于实时应用。本文提出绕过过度拟合过程，用编码器网络补充解码器来降低Cool - chic的编码复杂度。所提出的非过度拟合（N - O）Cool - chic相比Cool - chic编码复杂度显著降低1000倍，同时保持竞争力。
英文摘要：
Overfitted image codecs achieve strong compression performance and low decoder complexity by learning a lightweight decoder for each image. Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel. However, the encoding time associated with overfitted codecs may be prohibitively long for real-time applications, posing a challenge to their practical implementation in such scenarios. To address this issue, this paper proposes to decrease the encoding complexity of Cool-chic by bypassing the overfitting procedure and complementing the decoder with an encoder network. The proposed non-overfitted (N-O) Cool-chic, significantly reduces encoding complexity by a factor of 1000 compared to Cool-chic, while maintaining competitive performance.
