E partnership amongst the model parameters neural network [34,35] to discover the
E partnership between the model parameters neural network [34,35] to understand the mapping connection in between by hand parameters can visualize 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 can think about that the model (21) would bethe bit-rate is low, so we select the details entropy H 0,bit = four having a quantization bitdepth of four as a feature. Since the CS measurement in the image is sampled block by block, we take the image block because the video frame and design and style two image features in line with the video functions in reference [23]. By way of example, block difference (BD): the mean (and common deviation) of your distinction in between the measurements of adjacent blocks, i.e., 11 of 21 BD and BD . We also take the mean of measurements y0 as a feature. We designed a network such as 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 like 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 = four ]T Figure 8.2 uu j = [0 , y0u jf-maxdy-0 ), , min (j )4BD , BD , H0,bit=4 ] (23) 1 = g (W j -1 , 1 + ( j 1 ) f 2 y0 , (23) u ju = g(d j-1 u j= 1 + d j-1 ) , 2 j four 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 ) may be the sigmoid activation function, u j may be the input variable vector at the jwhere F would be the sigmoid activation , k ] . W d would be the network parameters learned th layer,g(v) may be the parameters vector [kfunction,j ,u j j would be the input variable vector at the j-th 1 2 layer, F is the parameters vector [k1 , k2 ]. Wj , d j will be the network parameters learned from from 3-Chloro-5-hydroxybenzoic acid web offline information. We take the imply square error (MSE) as the loss function. offline information. We take the mean square error (MSE) because the loss function. TEntropy 2021, 23,yf max ( y0 )f min ( y0 )kkBDBDHinput layer 1st hidden layer 2 nd hidden layer output layerFigure Four-layer feed-forward neural network model for the parameters. Figure eight.8. Four-layer feed-forward neural network model for the parameters.5. A General Rate-Distortion Optimization Approach for Sampling Rate and Bit-Depth five. A General Rate-Distortion Optimization System for Sampling Price and Bit-Depth five.1. Sampling Rate Modification 5.1. Sampling Rate Modification model parameters by minimizing the imply square error with the model (16) obtains theThe model (16) obtains the the total error is the smallest, you will find still square error all instruction samples. While model parameters by minimizing the meansome 2-Bromo-6-nitrophenol manufacturer samples of all coaching samples. Despite the fact that the total error is the smallest, there are actually nevertheless some samples with substantial errors. To stop excessive errors in predicting sampling price, we propose with typical codeword To stop excessive errors in predicting sampling rate, we prothe significant errors. length boundary and sampling rate boundary. pose the typical codeword length boundary and sampling rate boundary. 5.1.1. Average Codeword Length Boundary five.1.1. Typical Codeword bit-depth is determined, the average codeword length ordinarily When the optimal Length Boundary decreases the optimal bit-depth is determined, the average codeword length usually deWhen together with the sampling rate boost. While the average codeword.