本论文着重对整数变换与量化、去方块滤波做了研究。
In this dissertation, the integer transform, quantization, and deblocking filter are expatiated.
给出了积温效应的量化公式，提出了考虑积温效应的小波变换与神经网络负荷组合预测方法。
The quantitative formula of accumulated temperature effect is given and the load forecast combining wavelet transform and neural network is proposed with the accumulated temperature effect.
实验结果表明，该算法的性能优于多相变换与选择量化算法。
Simulation results verified the performance of the proposed algorithm better than that of the polyphase transform and selective quantization.
通过与传统的离散余弦变换（DCT）进行比较，表明了APIDCBT算法可以不用量化表，节省运算时间，与DCT可达到同样的压缩效果，并且在低码率情况下优于DCT。
This transform algorithm saves the operation time due to without quantization. It can get the same compressed result and even better performance in the case of low bit rate compared with DCT.
通过与传统的离散余弦变换（DCT）进行比较，表明了APIDCBT算法可以不用量化表，节省运算时间，与DCT可达到同样的压缩效果，并且在低码率情况下优于DCT。
This transform algorithm saves the operation time due to without quantization. It can get the same compressed result and even better performance in the case of low bit rate compared with DCT.
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