Visualization of numerical results in computer communications is very important such that some very small differences are sometimes crucial, distinguishable, and descriptive for comparison among some state-of-the-art techniques. For the issue of data quality evaluation and compression rates in internet of multimedia things, there are many metrics traditionally, for instance, peak signal-to-noise ratio (PSNR) is strongly able to describe non-sensitive (and relatively ambiguous) results of mean square error and since PSNR is normally between 10 and 100 for most of the lossy techniques, it can plotted with using any graphical/visualization tool. However, the results of compression rates for aggregation techniques may be a little complicated on which using a non-flexible mathematical operator like logarithm may have an unsuitable effect with ignoring the small differences while plotting the results. The aim behind this paper is to introduce a new metric entitled average capacity index (ACI), as a non-linear visualization approach/scaling mechanism, to be usable in evaluating capacity results of data hiding and aggregation algorithms based on bar charts. Some examples with synthetic and real data will show that the proposed metric outperforms the existing conventional tools in terms of statistical measures and visual presentation.

在计算机通信中，数值结果的可视化非常重要，因此，某些非常小的差异有时对于某些最新技术之间的比较而言至关重要，可区分且具有描述性。对于多媒体物联网中的数据质量评估和压缩率问题，传统上有很多度量标准，例如，峰值信噪比（PSNR）能够强烈描述非敏感（相对模棱两可）的结果。均方误差，并且由于大多数有损技术的PSNR通常在10到100之间，因此可以使用任何图形/可视化工具进行绘制。然而，聚合技术的压缩率结果可能会有些复杂，在使用诸如对数的非灵活数学运算符时，如果在绘制结果时忽略了小的差异，可能会产生不合适的效果。本文的目的是引入一种称为平均容量指数（ACI）的新指标，作为一种非线性可视化方法/缩放机制，可用于评估基于条形图的数据隐藏和聚合算法的容量结果。一些带有合成数据和真实数据的示例将表明，在统计量度和视觉呈现方面，拟议的指标优于现有的常规工具。作为一种非线性可视化方法/缩放机制，可用于评估基于条形图的数据隐藏和聚合算法的容量结果。一些带有合成数据和真实数据的示例将表明，在统计量度和视觉呈现方面，拟议的指标优于现有的常规工具。作为一种非线性可视化方法/缩放机制，可用于评估基于条形图的数据隐藏和聚合算法的容量结果。一些带有合成数据和真实数据的示例将表明，在统计量度和视觉呈现方面，拟议的指标优于现有的常规工具。