We propose a practical approach to JPEG image decoding, utilizing a local
implicit neural representation with continuous cosine formulation. The JPEG
algorithm significantly quantizes discrete cosine transform (DCT) spectra to
achieve a high compression rate, inevitably resulting in quality degradation
while encoding an image. We have designed a continuous cosine spectrum
estimator to address the quality degradation issue that restores the distorted
spectrum. By leveraging local DCT formulations, our network has the privilege
to exploit dequantization and upsampling simultaneously. Our proposed model
enables decoding compressed images directly across different quality factors
using a single pre-trained model without relying on a conventional JPEG
decoder. As a result, our proposed network achieves state-of-the-art
performance in flexible color image JPEG artifact removal tasks. Our source
code is available at this https URL

我们提出了一种实用的 JPEG 图像解码方法，利用具有连续余弦公式的本地隐式神经表示。我们的模型在不同质量因素下能直接对解压缩的图像进行解码，而无需依赖传统的 JPEG 解码器，从而在灵活的彩色图像 JPEG 伪影去除任务中实现了最先进的性能。

JDEC: 增强连续余弦系数的 JPEG 解码

JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients

Watermarking generative-AI systems, such as LLMs, has gained considerable
interest, driven by their enhanced capabilities across a wide range of tasks.
Although current approaches have demonstrated that small, context-dependent
shifts in the word distributions can be used to apply and detect watermarks,
there has been little work in analyzing the impact that these perturbations
have on the quality of generated texts. Balancing high detectability with
minimal performance degradation is crucial in terms of selecting the
appropriate watermarking setting; therefore this paper proposes a simple
analysis framework where comparative assessment, a flexible NLG evaluation
framework, is used to assess the quality degradation caused by a particular
watermark setting. We demonstrate that our framework provides easy
visualization of the quality-detection trade-off of watermark settings,
enabling a simple solution to find an LLM watermark operating point that
provides a well-balanced performance. This approach is applied to two different
summarization systems and a translation system, enabling cross-model analysis
for a task, and cross-task analysis.

通过比较评估的灵活自然语言生成评估框架，本研究提出了一个简单的分析框架，用于评估特定水印设置引起的生成文本质量降低，以找到平衡性能和易检测性的最佳水印操作点。该方法应用于两个不同的摘要系统和一个翻译系统，实现了任务内和跨任务的模型分析。