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A unified framework for image compression and segmentation by using an incremental neural network

机译：使用增量神经网络进行图像压缩和分割的统一框架

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This paper presents a novel unified framework for compression and decision making by using artificial neural networks. The proposed framework is applied to medical images like magnetic resonance (MR), computer tomography (CT) head images and ultrasound image. Two artificial neural networks, Kohonen map and incremental self-organizing map (ISOM), are comparatively examined. Compression and decision making processes are simultaneously realized by using artificial neural networks. In the proposed method, the image is first decomposed into blocks of 8 × 8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of the DCT coefficients vectors (codewords) is reduced by low-pass filtering. This way of dimension reduction is known as vector quantization in the compression scheme. Codewords are the feature vectors for the decision making process. It is observed that the proposed method gives higher compression rates with high signal to noise ratio compared to the JPEG standard, and also provides support in decision-making by performing segmentation.
机译：本文提出了一种使用人工神经网络进行压缩和决策的新型统一框架。所提出的框架适用于医学图像，例如磁共振（MR），计算机断层扫描（CT）头部图像和超声图像。比较了两个人工神经网络，Kohonen映射和增量自组织映射（ISOM）。通过使用人工神经网络可以同时实现压缩和决策过程。在提出的方法中，图像首先分解为8×8像素的块。为每个块计算二维离散余弦变换（2D-DCT）系数。 DCT系数矢量（代码字）的维数通过低通滤波来减小。这种降维方法在压缩方案中被称为矢量量化。码字是决策过程的特征向量。可以看出，与JPEG标准相比，所提出的方法具有更高的压缩率和更高的信噪比，并且还通过执行分段为决策提供了支持。

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