References Image Compression by Edge Pattern Learning Using Multilayer P Erc E Ptro N S
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Improved neural structures for image compression
The problem considered is the effective compression of image data. Compared to the many methods which allow for high compression factors (mainly based on the DCT), the proposed neural approaches offer, after a suitable training, comparable performance both in terms of perceived image quality and measured SNR. Moreover, they are able to perform in the same step the two main required operations of transformation and selection of the most significant terms, with a correspondingly smaller computational effort.<<ETX>>
A neural network approach to transform image coding
A neural network approach is presented for transform image coding. It is shown that the three steps in the conventional transform image coding, i.e. the unitary transform of spatial domain image data, the quantization of the transform domain data and the binary coding of the quantized data, can be unified into a one-step optimization problem. Then, the optimization problem is solved by an appropriately constructed Hopfield neural network whose input is the spatial domain image data and whose output is binary codes. A practical circuit implementation is given to perform the transform image coding. the circuit has rM2 neurons, where r is the bit-rate, in bit/pixel, of the coding and M2 is the size of the images. Each neuron consists of only a non-linear voltage amplifier, a linear voltage-controlled current source, a d.c. current source, a linear passive resistor, a linear passive capacitor, and a weighted voltage summer which can be made of a single op amp with some linear passive resistors. Moreover, each neuron is locally connected with no more than b - 1 other neurons by wires, where b is the maximum bit allocated to a transform domain coefficient. Therefore, our proposed approach is particularly suitable for low-bit-rate image coding and VLSI implementation. Furthermore, the analogue and parallel nature of our approach matches perfectly the high-speed requirement of real-time image coding.