E connection amongst the model parameters neural network [34,35] to find out the
E partnership in between the model parameters neural network [34,35] to understand the mapping partnership amongst by hand parameters can DMPO custom synthesis consider that in lieu of designing be function connection the model [36,37]. We and image featuresthe model (21) would the function relationship by hand [36,37]. We are able to picture that the model (21) would bethe bit-rate is low, so we choose the information and facts entropy H 0,bit = four with a quantization bitdepth of four as a function. Since the CS measurement from the image is sampled block by block, we take the image block because the video frame and design and style two image characteristics based on the video options in reference [23]. For instance, block distinction (BD): the imply (and normal deviation) of your distinction amongst the measurements of adjacent blocks, i.e., 11 of 21 BD and BD . We also take the mean of measurements y0 as a function. We made a network which includes an input layer of seven neurons and an output layer of two neurons to estimate the model parameters [k1 , k2 ] , as shown in Formula (23) We designed a network including an input layer of seven neurons and an output layer andtwo neurons to estimate the model parameters [k , k ], as shown in Formula (23) and of Figure 8. 1 two 2 u1 = [ 0 , y0 , f max ( y0 ) , f min ( y0 ) , BD , BD , H 0,bit = 4 ]T Figure 8.two uu j = [0 , y0u jf-maxdy-0 ), , min (j )4BD , BD , H0,bit=4 ] (23) 1 = g (W j -1 , 1 + ( j 1 ) f two y0 , (23) u ju = g(d j-1 u j= 1 + d j-1 ) , two j 4 W , j -4 F = W j -1 j -1 + j -1 F = Wj-1 u j-1 + d j-1 , j = 4 exactly where g (v ) could be the sigmoid activation function, u j could be the input variable vector at the jwhere F would be the sigmoid activation , k ] . W d will be the network parameters learned th layer,g(v) could be the parameters vector [kfunction,j ,u j j would be the input variable vector at the j-th 1 two layer, F is the parameters vector [k1 , k2 ]. Wj , d j are the network parameters learned from from offline data. We take the imply square error (MSE) because the loss function. offline information. We take the imply square error (MSE) because the loss function. TEntropy 2021, 23,yf max ( y0 )f min ( y0 )kkBDBDHinput layer 1st hidden layer two nd hidden layer output layerFigure Four-layer feed-forward neural network model for the parameters. Figure 8.8. Four-layer feed-forward neural network model for the parameters.5. A General Rate-Distortion Optimization Strategy for Sampling Price and Bit-Depth five. A Common Rate-Distortion Optimization Method for Sampling Rate and Bit-Depth 5.1. Sampling Price Modification 5.1. Sampling Rate Modification model parameters by minimizing the imply square error of your model (16) obtains theThe model (16) obtains the the total error will be the smallest, there are actually nevertheless square error all training samples. Despite the fact that model parameters by minimizing the meansome samples of all instruction samples. Although the total error could be the smallest, there are nevertheless some samples with important errors. To prevent excessive errors in predicting sampling price, we propose with average codeword To prevent excessive errors in predicting sampling rate, we prothe substantial errors. Nitrocefin Autophagy Length boundary and sampling price boundary. pose the average codeword length boundary and sampling price boundary. five.1.1. Average Codeword Length Boundary five.1.1. Average Codeword bit-depth is determined, the average codeword length commonly When the optimal Length Boundary decreases the optimal bit-depth is determined, the average codeword length normally deWhen with the sampling rate boost. Despite the fact that the average codeword.
