Systems and methods for processing online data are disclosed. One such method includes receiving a plurality of data points in a time-series at a short term storage. The method also includes calculating at least one approximation coefficient based on the plurality of data points using a wavelet transform, including calculating a highest level approximation coefficient, and calculating estimated value based on the highest level approximation coefficient. The method further includes calculating differences between the estimated value and the plurality of data points of the short term storage, and determining whether a maximum difference among the calculated differences is less than a predetermined threshold. The method further includes, based on the maximum difference being greater than or equal to the predetermined threshold, storing the oldest data point of the short term storage in a long term storage.

A method for compressing display data is disclosed. The method comprises performing a wavelet transformation to obtain a general approximation coefficient and a plurality of detail coefficients for a group of pixels; determining whether to prioritise transmission of the general approximation coefficient over transmission of the detail coefficients based on whether there are sufficient resources available to enable a corresponding image frame to be ready for display and/or based on a time since the detail coefficients for a corresponding group of pixels were previously transmitted. If it is determined that transmission of the general approximation coefficient is to be prioritised over transmission of the detail coefficients, the method comprises either: transmitting only the general approximation coefficient, or compressing at least one detail coefficient so as to be more compressed than the general approximation coefficient and transmitting the general approximation coefficient and the at least one compressed detail coefficient.

A method for compressing display data is disclosed. The method comprises performing a wavelet transformation to obtain a general approximation coefficient and a plurality of detail coefficients for a group of pixels; determining whether to prioritise transmission of the general approximation coefficient over transmission of the detail coefficients based on whether there are sufficient resources available to enable a corresponding image frame to be ready for display and/or based on a time since the detail coefficients for a corresponding group of pixels were previously transmitted. If it is determined that transmission of the general approximation coefficient is to be prioritised over transmission of the detail coefficients, the method comprises either: transmitting only the general approximation coefficient, or compressing at least one detail coefficient so as to be more compressed than the general approximation coefficient and transmitting the general approximation coefficient and the at least one compressed detail coefficient.

Systems and methods for processing online data are disclosed. One such method includes receiving a plurality of data points in a time-series at a short term storage. The method also includes calculating at least one approximation coefficient based on the plurality of data points using a wavelet transform, including calculating a highest level approximation coefficient, and calculating estimated value based on the highest level approximation coefficient. The method further includes calculating differences between the estimated value and the plurality of data points of the short term storage, and determining whether a maximum difference among the calculated differences is less than a predetermined threshold. The method further includes, based on the maximum difference being greater than or equal to the predetermined threshold, storing the oldest data point of the short term storage in a long term storage.

An image processing method and apparatus in the field of artificial intelligence are disclosed. The method comprises: decomposing a first image to obtain a first structure sub-image and a first detail sub-image, where the first image is any frame of image in video data other than a first frame; fusing first hidden state information and the first structure sub-image to obtain a second structure sub-image, and splicing the first hidden state information and the first detail sub-image to obtain a second detail sub-image; performing feature extraction based on the second structure sub-image and the second detail sub-image to obtain a structure feature and a detail feature; and obtaining an output image based on the structure feature and the detail feature, where a resolution of the output image is higher than a resolution of the first image.