The K-SOM has been successfully use

The K-SOM has been successfully used in quantizing
digital images. Many hardware realizations of the K-SOM
quantizer have been published in order to speed up part or
all of the operations with similar drawbacks of poor
resulting image quality compared to software counterparts.
Mainly, this resulted from the fact that to make the system
compact and be synthesizable on a single chip FPGA, the
internal data representations and operations within the
hardware platforms are required to be all integer based. In
this paper, we proposed a hardware centric K-SOM quantizer
algorithm which relies on a rational-based representation
of the codebook and learning kernel. This extends
the capability of the quantizer to accept an approximated
non-linear learning kernel. The experimental results proved
that the quality of the outcome images was superior to
previous implementations with an acceptable throughput.
The resource utilizations and frame rate throughput of the
proposed approach were just a bit worse than the predecessor
implementations. It was, however, still possible to
synthesize the whole system onto a single moderate density
FPGA. By following the proposed algorithm, it opens an
opportunity for a hardware quantizer to accept different

Results (English) 3:

的k-som已成功地用于量化数字图像。该k-som许多硬件实现量化已经公布，为了加快部分或所有的操作都有类似的缺点由此产生的图像质量相比，软件同行。主要是，这一事实导致了系统紧凑，可综合在一个单芯片的FPGA，内部数据表示和操作硬件平台是必需的，是基于整数的。在本文中，我们提出了一个以硬件为中心的k-som量化器算法，它依赖于一个基于理性的表示的码本和学习内核。这延伸量化的能力去接受一个近似非线性学习核。实验结果证明结果图像的质量优于具有可接受的吞吐量的以前的实现。资源的利用率和帧速率的吞吐量提出的方法只是有点比前任更糟糕实现。然而，它仍然有可能合成整个系统到一个单一的中等密度FPGA。通过以下所提出的算法，它打开一个对于硬件量化器接受不同的机会