System and method for compressing video using deep learning

A video compressing device using deep learning, the video compressing device comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
• segment each of a plurality of frames associated with a video into a plurality of super blocks based on an element present in each of the plurality of frames and a motion associated with the element, wherein the segmentation of the plurality of frames into the plurality of super blocks is of variable shape and size;
• determine a block size for partition of each of the plurality of super blocks into a plurality of sub blocks, based on a feature of each of the plurality of super blocks using a Convolutional Neural Network (CNN), wherein the feature comprises at least one of a size of the super block and a motion related information;
• generate a prediction data for each of the plurality of sub blocks based on a motion vector predicted and learned by the CNN, wherein the CNN predicts the motion vector based on a co-located frames;
• determine a residual data for each of the plurality of sub blocks by subtracting the prediction data from an associated original data, wherein the associated original data is a bit stream of each of the plurality of sub blocks;
• and
• generate a transformed quantized residual data using each of a transformation algorithm and a quantization algorithm based on a plurality of parameters associated with the residual data, wherein the plurality of parameters comprises the compression rate and Signal to noise ratio.

A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:

• segmenting each of a plurality of frames associated with a video into a plurality of super blocks based on an element present in each of the plurality of frames and a motion associated with the element, wherein the segmentation of the plurality of frames into the plurality super blocks is of variable shape and size;
• determining a block size for partition of each of the plurality of super blocks into a plurality of sub blocks, based on a feature of each of the plurality of super blocks using a Convolutional Neural Network (CNN), wherein the feature comprises at least one of a size of the super block and a motion related information;
• generating a prediction data for each of the plurality of sub blocks based on a motion vector predicted and learned by the CNN, wherein the CNN predicts the motion vector based on a co-located frames;
• determining a residual data for each of the plurality of sub blocks by subtracting the prediction data from an associated original data, wherein the associated original data is a bit stream of each of the plurality of sub blocks;
• and
• generating a transformed quantized residual data using each of a transformation algorithm and a quantization algorithm based on a plurality of parameters associated with the residual data, wherein the plurality of parameters comprises the compression rate and Signal to noise ratio.