Systems and methods for perceptual quality-driven adaptive quantization in neural network data compression with dynamic feedback control

1 . A computer system comprising:

a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:

preprocess raw data to generate input data;

analyze content characteristics of the input data to determine initial quantization parameters;

perform content-aware quantization on the input data using the determined quantization parameters;

perform a discrete cosine transform operation on the quantized input data to create a plurality of subbands;

compress the plurality of subbands using an encoder within a multi-layer autoencoder to generate compressed data;

perform at least one additional compression pass on the plurality of subbands using the encoder within the multi-layer autoencoder to generate at least one additional compressed bitstream, and multiplex the compressed data and the at least one additional compressed bitstream into a combined bitstream;

collect quality metrics from the combined bitstream, the quality metrics comprising at least a perceptual quality score and a compression measure of the compressed data;

store the quality metrics in a quality history database, wherein the quality history database may be accessed during a future compression of a second plurality of subbands;

generate parameter adjustment signals, wherein the parameter adjustment signals are based on the collected quality metrics and a quality assessment analysis;

modify the quantization parameters based on the parameter adjustment signals to create optimized quantization parameters for subsequent compression operations while implementing gradual parameter updates to maintain stability;

decompress the combined bitstream using a decoder within the multi-layer autoencoder to obtain reduced output data; and

process the reduced output data through a context recovery network to recover information lost in compression, thereby generating restored output data.

2 . The computer system of claim 1 , wherein the context recovery network comprises a first recovery stage associated with a first low frequency group, a second recovery stage associated with a second low frequency group, and a third recovery stage associated with a high frequency group.

3 . The computer system of claim 2 , wherein the first recovery stage, second recovery stage, and third recovery stage each implement a respective loss function optimized for data restoration.

4 . The computer system of claim 1 , wherein the raw data comprises IoT sensor data or hyperspectral data.

5 . The computer system of claim 1 , wherein analyzing the content characteristics comprises extracting perceptual feature information from the input data and determining content complexity metrics for different portions of the input data.

6 . The computer system of claim 1 , wherein performing the content-aware quantization comprises applying different quantization parameters to different regions of the input data based on content complexity and perceptual importance.

7 . The computer system of claim 1 , wherein the quality metrics comprise perceptual quality scores and compression ratios calculated for the combined bitstream, and further comprising maintaining a quality history database for parameter adjustment decisions.

8 . The computer system of claim 1 , wherein modifying the quantization parameters comprises implementing gradual parameter adjustments based on the quality metrics to maintain stability.

9 . A method for compressing and restoring data, comprising:

preprocessing raw data to generate input data;

analyzing content characteristics of the input data to determine initial quantization parameters;

performing content-aware quantization on the input data using the determined quantization parameters;

performing a discrete cosine transform operation on the quantized input data to create a plurality of subbands;

compressing the plurality of subbands using an encoder within a multi-layer autoencoder to generate compressed data;

performing at least one additional compression pass on the plurality of subbands using the encoder within the multi-layer autoencoder to generate at least one additional latent space representation, and multiplexing the latent space representation and the at least one additional latent space representation into a combined bitstream;

collecting quality metrics from the combined bitstream, the quality metrics comprising at least a perceptual quality score and a compression measure of the compressed data;

storing the quality metrics in a quality history database, wherein the quality history database may be accessed during a future compression of a second plurality of subbands;

generating parameter adjustment signals, wherein the parameter adjustment signals are based on the collected quality metrics and a quality assessment analysis;

modifying the quantization parameters based on the parameter adjustment signals to create optimized quantization parameters for subsequent compression operations while implementing gradual parameter updates to maintain stability;

decompressing the combined bitstream using a decoder within the multi-layer autoencoder to obtain reduced output data; and

processing the reduced output data through a context recovery network to recover information lost in compression, thereby generating restored output data.

10 . The method of claim 9 , wherein the multi-layer autoencoder comprises a first kernel configured with five channels and a stride value of 1 that provides output to a first plurality of residual blocks, which in turn provides output to a first attention network, followed by a second plurality of residual blocks providing output to a second kernel configured with five channels and a stride value of 2, which ultimately provides output to a second attention network.

11 . The method of claim 9 , wherein processing the reduced output data through the context recovery network comprises implementing three loss functions respectively associated with first low frequency, second low frequency, and high frequency groups, wherein at least one of the loss functions employs a weighting scheme optimized for data restoration.

12 . The method of claim 9 , wherein analyzing the content characteristics comprises performing multi-scale analysis of the input data and identifying regions of varying perceptual importance.

13 . The method of claim 9 , wherein performing the content-aware quantization comprises distributing available bits across portions of the input data based on the analyzed content characteristics, prioritizing regions of higher perceptual importance.

14 . The method of claim 9 , wherein collecting the quality metrics comprises calculating perceptual quality scores and monitoring compression ratios for the combined bitstream.

15 . The method of claim 9 , wherein modifying the quantization parameters comprises generating parameter adjustment signals based on the collected quality metrics and implementing a predictive control algorithm.

16 . The method of claim 9 , wherein collecting the quality metrics occurs at multiple points during compression of the input data to enable comprehensive quality control.