Controllable lossy compression system using joint learning
A system and method for controllable lossy data compression employing a joint learning framework to efficiently compress and reconstruct input data while balancing compression ratio and reconstruction quality. The system comprises an encoding system, a temporal modeling system, and a decoding system, which are jointly optimized to minimize a combined loss function. The encoding system, such as a Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input data into a compact representation, while introducing a controllable degree of lossy compression based on adjustable compression parameters. The temporal modeling system, such as a Multilayer Perceptron Long Short-Term Memory captures temporal dependencies in the compressed representation. The decoding system, such as a VQ-VAE decoder, reconstructs the input data from the compressed representation. By providing control over the trade-off between compression ratio and reconstruction quality, the system offers flexibility for diverse applications.
1. A system for controllable lossy compression, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions stored in the memory and operable on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
encode input data into a compressed representation using an encoding system;
introduce a controllable degree of lossy compression to the compressed representation based on one or more compression parameters;
model temporal dependencies in the compressed representation using a temporal modeling system;
reconstruct the input data from the compressed representation using a decoding system; and
jointly optimize the encoding system, the temporal modeling system, and the decoding system to minimize a joint loss function.
2. The system of claim 1 , wherein the encoding system comprises a Vector Quantized Variational Autoencoder (VQ-VAE) encoder.
3. The system of claim 2 , wherein the one or more compression parameters include a size of a learned codebook used by the VQ-VAE encoder for quantization.
4. The system of claim 1 , wherein the temporal modeling system comprises a Multilayer Perceptron Long Short-Term Memory system.
5. The system of claim 1 , wherein the decoding system comprises a Vector Quantized Variational Autoencoder decoder.
6. The system of claim 1 , wherein the joint loss function comprises a combination of reconstruction loss, quantization loss, and temporal modeling loss.
7. The system of claim 1 , wherein the input data comprises one or more of image data, video data, audio data, and time-series data.
8. The system of claim 1 , wherein the computing device is further caused to preprocess the input data using a data preprocessing system prior to encoding the input data.
9. The system of claim 1 , wherein the computing device is further caused to post-process the reconstructed input data using a data post-processing system.
10. The system of claim 1 , wherein the one or more compression parameters are adjustable based on a desired trade-off between compression ratio and reconstruction quality.
11. A method for controllable lossy compression, comprising the steps of:
encoding input data into a compressed representation using an encoding system;
introducing a controllable degree of lossy compression to the compressed representation based on one or more compression parameters;
modeling temporal dependencies in the compressed representation using a temporal modeling system;
reconstructing the input data from the compressed representation using a decoding system; and
jointly optimizing the encoding system, the temporal modeling system, and the decoding system to minimize a joint loss function.
12. The method of claim 11 , wherein the encoding system comprises a Vector Quantized Variational Autoencoder (VQ-VAE) encoder.
13. The method of claim 12 , wherein the one or more compression parameters include a size of a learned codebook used by the VQ-VAE encoder for quantization.
14. The method of claim 11 , wherein the temporal modeling system comprises a Multilayer Perceptron Long Short-Term Memory system.
15. The method of claim 11 , wherein the decoding system comprises a Vector Quantized Variational Autoencoder decoder.
16. The method of claim 11 , wherein the joint loss function comprises a combination of reconstruction loss, quantization loss, and temporal modeling loss.
17. The method of claim 11 , wherein the input data comprises one or more of image data, video data, audio data, and time-series data.
18. The method of claim 11 , further comprising the step of preprocessing the input data using a data preprocessing system prior to encoding the input data.
19. The method of claim 11 , further comprising the step of post-processing the reconstructed input data using a data post-processing system.
20. The method of claim 11 , wherein the one or more compression parameters are adjustable based on a desired trade-off between compression ratio and reconstruction quality.
21. Non-transitory, computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system employing a controllable lossy compression system, cause the computing system to:
encode input data into a compressed representation using an encoding system;
introduce a controllable degree of lossy compression to the compressed representation based on one or more compression parameters;
model temporal dependencies in the compressed representation using a temporal modeling system;
reconstruct the input data from the compressed representation using a decoding system; and
jointly optimize the encoding system, the temporal modeling system, and the decoding system to minimize a joint loss function.
22. The non-transitory, computer-readable storage media of claim 21 , wherein the encoding system comprises a Vector Quantized Variational Autoencoder (VQ-VAE) encoder.
23. The non-transitory, computer-readable storage media of claim 22 , wherein the one or more compression parameters include a size of a learned codebook used by the VQ-VAE encoder for quantization.
24. The non-transitory, computer-readable storage media of claim 21 , wherein the temporal modeling system comprises a Multilayer Perceptron Long Short-Term Memory system.
25. The non-transitory, computer-readable storage media of claim 21 , wherein the decoding system comprises a Vector Quantized Variational Autoencoder decoder.
26. The non-transitory, computer-readable storage media of claim 21 , wherein the joint loss function comprises a combination of reconstruction loss, quantization loss, and temporal modeling loss.
27. The non-transitory, computer-readable storage media of claim 21 , wherein the input data comprises one or more of image data, video data, audio data, and time-series data.
28. The non-transitory, computer-readable storage media of claim 21 , wherein the computing system is further caused to preprocess the input data using a data preprocessing system prior to encoding the input data.
29. The non-transitory, computer-readable storage media of claim 21 , wherein the computing system is further caused to post-process the reconstructed input data using a data post-processing system.
30. The non-transitory, computer-readable storage media of claim 21 , wherein the one or more compression parameters are adjustable based on a desired trade-off between compression ratio and reconstruction quality.