SYSTEMS AND METHODS FOR DISTORTION REMOVAL AT MULTIPLE QUALITY LEVELS

1. A computer-implemented method, comprising: receiving training content, the training content comprising: original content; reconstructed content, wherein the reconstructed content is derived from distorted original content; and training distortion quality levels corresponding to the reconstructed content; receiving an initial distortion removal model; generating a conditioned distortion removal model by training the initial distortion removal model using the training content; and storing the conditioned distortion removal model. 2. The computer-implemented method of claim 1, further comprising: receiving target content, wherein the target content has one or more target distortion quality levels; and applying the conditioned distortion removal model to the target content to generate corrected target content. 3. The computer-implemented method of claim 1, wherein the initial distortion removal model and the conditioned distortion removal model comprise one or more user-defined output branches based on one or more distortion quality levels. 4. The computer-implemented method of claim 3, wherein the initial distortion removal model comprises two branches, wherein a first set of training content corresponding to a first branch passes through a first set of convolutional layers, and a second set of training content corresponding to a second branch passes through a first set of convolutional layers and a second set of convolutional layers. 5. The computer-implemented method of claim 4, wherein training the initial distortion removal model using the training content comprises: applying the first set of training content to the first set of convolutional layers; applying at least one of the first set of training content to the second set of convolution layers; applying the second set of training content to the first set and the second set of convolutional layers; and when the second branch is conditioned, training the first branch and the second branch with equal weighting for the first branch and the second branch. 6. The computer-implemented method of claim 2, wherein the training distortion quality levels includes at least one different individual value compared to the one or more target distortion quality levels. 7. The computer-implemented method of claim 1, wherein the initial distortion removal model and the conditioned distortion removal model comprise a convolutional neural network. 8. The computer-implemented method of claim 1, wherein the initial distortion removal model and the conditioned distortion removal model comprise an activation function. 9. The computer-implemented method of claim 1, wherein the conditioned distortion model is trained to remove distortions from target content. 10. A computer-implemented method, comprising: receiving target content, wherein the target content has multiple distortion quality levels; receiving a conditioned distortion removal model, the conditioned distortion removal model having been conditioned by training an initial distortion removal model using training content, wherein the training content comprises original content and corresponding reconstructed content, each reconstructed content having a given distortion quality level; and applying the conditioned distortion removal model to the target content to generate corrected target content. 11. The computer-implemented method of claim 10, wherein the conditioned distortion removal model comprises one or more user-defined output branches based on the multiple distortion quality levels. 12. The computer-implemented method of claim 11, wherein the target content is corrected based on a given distortion quality level of a given target content corresponding to one of the one or more user-defined output branches of the conditioned distortion removal model. 13. The computer-implemented method of claim 11, wherein the conditioned distortion removal model comprises two branches, wherein a first set of target content corresponding to a first branch passes through a first set of convolutional layers, and a second set of target content corresponding to a second branch passes through a first set of convolutional layers and a second set of convolutional layers. 14. The computer-implemented method of claim 10, wherein the initial distortion removal model and the conditioned distortion removal model comprise a convolutional neural network. 15. The computer-implemented method of claim 10, wherein the content comprises one or more of an image and a video. 16. The computer-implemented method of claim 15, wherein the content comprises one or more of standard content, high definition (HD) content, ultra HD (UHD) content, 4k UHD content, and 8k UHD content. 17. A system, comprising: electronic storage; one or more physical computer processors configured by machine-readable instructions to: obtain target media content, wherein the target media content has multiple distortion quality levels; obtain, from the electronic storage, a conditioned distortion removal model, the conditioned distortion removal model having been conditioned by training an initial distortion removal model using training media content, wherein the training media content comprises original media content and corresponding reconstructed media content, each reconstructed media content having a given distortion quality level; and apply the conditioned distortion removal model to the target media content to generate corrected target media content using the one or more physical computer processors. 18. The system of claim 17, wherein the conditioned distortion removal model comprises one or more user-defined output branches based on one or more distortion quality levels. 19. The system of claim 18, wherein the target content is corrected based on a given distortion quality level of a given target content corresponding to one of the one or more branches of the conditioned distortion removal model. 20. The system of claim 17, wherein the initial distortion removal model and the conditioned distortion removal model comprise a convolutional neural network.