Roposed bit-rate model are 0 two , H0 , f max (y0 ) f min (y
Roposed bit-rate model are 0 2 , H0 , f max (y0 ) f min (y0 ). A finite set of true numbers typically requires to become quantized just before calculating the details entropy. The optimal PX-478 Description bit-depth of quite a few pictures is low when the bit-rate is low, so we pick the facts entropy H0,bit=4 using a quantization bit-depth of four as a function. Since the CS measurement of the image is sampled block by block, we take the image block because the video frame and design and style two image attributes in accordance with the video features in reference [23]. As an example, block difference (BD): the mean (and standardEntropy 2021, 23,ten ofEntropy 2021, 23, 1354 Entropy 2021, 23,11 of 23 11 ofdeviation) on the distinction in between the measurements of adjacent blocks, i.e., D and BD . We also take the imply of measurements y0 as a function.The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)6.five 6 6.5 5.5 six 5 5.5 4.5 five 4 four.five 3.5 4 three three.five two.five three 0 2.five(a) (a)The bit-depth (bit) The optimaloptimal bit-depth (bit)(b) (b)The bit-depth (bit) The optimaloptimal bit-depth (bit)(c) (c)The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)(d) (d)The bit-depth (bit) The optimaloptimal bit-depth (bit)0.2 0.0.4 0.0.6 0.0.eight 0.11.two 1.1.four 1.Bit-Rate (bpp) Bit-Rate (bpp)Actual worth Predicted value g(R) Actual value Predicted value 2 1.six 1.eight g(R)1.6 1.8Figure six. The predicted bit-depths of eight photos for the SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; (d) Boats; Figure six. The predicted bit-depths of eight images for the SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; (d) Boats; (e) Cameraman; (f) Foreman; (g) Residence; (h)photos for the SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; (d) Boats; Figure six. The predicted bit-depths of eight Lena. (e) Cameraman; (f) Foreman; (g) Home; (h) Lena. (e) Cameraman; (f) Foreman; (g) Property; (h) Lena.(e) (e)(f) (f)(g) (g)(h) (h)The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)(a) (a)The bit-depth (bit) The optimaloptimal bit-depth (bit) The bit-depth (bit) The optimaloptimal bit-depth (bit)(b) (b)The bit-depth (bit) The optimaloptimal bit-depth (bit)(c) (c)The bit-depth (bit) The optimaloptimal bit-depth (bit)The bit-depth (bit) The optimaloptimal bit-depth (bit)(d) (d)Figure 7. The predicted bit-depths of eight images for the DPCM-plus-SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; Figure 7. The predicted bit-depths of eight images for the DPCM-plus-SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; Figure 7. (e) predicted bit-depths of eight Property; (h) Lena. (d) Boats;TheCameraman; (f) Foreman; (g)pictures for the DPCM-plus-SQ framework. (a) Monarch; (b) Parrots; (c) Barbara; (d) Boats; (e) Cameraman; (f) Foreman; (g) Property; (h) Lena. (d) Boats; (e) Cameraman; (f) Foreman; (g) Residence; (h) Lena.(e) (e)(f) (f)(g) (g)(h) (h)four.2. Model Parameter Estimation Depending on 3-Chloro-5-hydroxybenzoic acid Formula Neural Network four.two. Model Parameter Estimation a function for estimating the model parameters accurately. It is challenging to style Determined by Neural Network It really is challenging to design a function for estimating the model parameters accurately. Consequently, we use a four-layer feed-forward neural network [34,35] to understand the mapping Hence, we use a four-layer feed-forwardand image functions rather than designing th.
