论文标题
内容自适应潜在和神经图像压缩的解码器
Content Adaptive Latents and Decoder for Neural Image Compression
论文作者
论文摘要
近年来，神经图像压缩（NIC）算法已显示出强大的编码性能。但是，其中大多数不适合图像内容。尽管已经通过更新编码器侧组件提出了几种内容自适应方法，但未充分利用了潜在和解码器的适应性。在这项工作中，我们提出了一个新的NIC框架，可改善潜在和解码器的内容适应性。具体来说，要删除潜伏期的冗余，我们的内容自适应通道掉落（CACD）方法会自动选择潜在的最佳质量水平，并删除冗余通道。此外，我们提出了内容自适应特征转换（CAFT）方法，以通过提取图像内容的特征信息来改善解码器端内容的适应性，然后将其用于转换解码器端的特征。实验结果表明，我们提出的用编码器侧更新算法的方法达到了最先进的性能。
In recent years, neural image compression (NIC) algorithms have shown powerful coding performance. However, most of them are not adaptive to the image content. Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited. In this work, we propose a new NIC framework that improves the content adaptability on both latents and the decoder. Specifically, to remove redundancy in the latents, our content adaptive channel dropping (CACD) method automatically selects the optimal quality levels for the latents spatially and drops the redundant channels. Additionally, we propose the content adaptive feature transformation (CAFT) method to improve decoder-side content adaptability by extracting the characteristic information of the image content, which is then used to transform the features in the decoder side. Experimental results demonstrate that our proposed methods with the encoder-side updating algorithm achieve the state-of-the-art performance.
