Deep Learning Approaches for Video Compression

September 14, 2022

Typical video compression focuses on interframe or intraframe compression using pairs of electrical circuits such as quantizers-dequantizers, transforms-reverse transformers, and so on However, It has recently been discovered that the contribution of video data created in big data has risen. extensively. Interdisciplinary techniques are assisting in the exploration of numerous possibilities of compression of video Deep learning-based techniques have recently gained popularity.

In this blog, we will describe what video compression is, why it is necessary, the typical way to compress videos and the deep learning approach. 

What is Video Compression

Video compression is the technique of compressing a video clip such that it takes up less space than the original file and is easier to send across a network like the Internet. It is a video compression technique that shrinks video file formats by removing unnecessary and non-functional material from the original video file.

Why compress the Videos

The biggest issue of anyone reducing video footage is generally file size because huge files slow down easy uploads, file transfers, flawless internet streaming, and so on. This is because they require more storage space and bandwidth, which might be expensive or limited on a video hosting site. 

A basic 1080 HD video clip, for example, can take up around 11 GB of space for each minute of video. This fluctuates significantly depending on the frame, but it's more space than most people can afford for a one-minute film. Because of this, video compression is essential. Video compression reduces storage capacity requirements while increasing file transfer and transmission speeds. This makes video uploading and sharing easier, allowing viewers to enjoy a more smooth streaming experience.

How Video Compression is Achieved

Video compression is often accomplished by deleting repeating images, sounds, and/or sequences from a video. For example, a video may repeat the same backdrop, picture, or sound, or the data displayed/attached to the video clip may be insignificant. To lower the size of the video file, video compression will eliminate all such material. When a video is compressed, its original format is converted to a new one (depending on the codec used). To play the video file, the video player must support that video format or be integrated with the compression codec.

Figure: Video compression Approaches

Deep Learning Approach for Video Compression

For video compression, there are numerous deep learning-based approaches. DNN techniques are more effective because they have numerous epochs that update (depending on the quantity and complexity of data) hyperparameters that aid in model training. It will be prepared to retrieve real-world data. 

We have observed various successful DNN-based picture compression algorithms that leverage highly nonlinear transformations and an end-to-end training strategy. Image compression methods have shown to be quite effective. The nice thing about the deep learning-based solution is that it uses classical compression architecture and a neural network with non-linear representation.

The following are ways to video compression based on DNN. 

  1. The first technique is to create an efficient video codec by modifying the evolutionary algorithm to create an appropriate codebook for adaptive vector quantization. This will be used as an example. a neural network activation function To do this, a background subtraction method is employed. remove motion items from the frame It aids in the creation of a first context-based codebook. 

For lossless compression, Differential Pulse Code Modulation (DPCM) is used. wavelet coefficients that are significant Neural networks that use Learning Vector Quantization (LVQ) Lossy compression with low energy coefficients are utilized. Run Length is the final phase. RLE encoding is used to attain a greater compression ratio.

  1. The second approach is a self-learning system for removing geometry artifacts in video-based point cloud compression to increase compression efficiency (V-PCC). This is the initial method for carrying out this procedure. It yields encouraging results when it comes to removing geometric artifacts and reconstructing 3D films. Later on, CNN can be used to increase the accuracy of video occupancy maps.
  1. The third approach is based on ConvGRU, a convolutional recurrent neural network that combines the benefits of both CNN and RNN. The system's randomized emission phase ConvGRU-based design improves performance and can aid in additional optimization upgrades.
  1. DeepPVC, an end-to-end deep predictive model, is used in the fourth approach for video compression. It decodes video data in parallel and outperforms AVC and HEVC. 

Advantages of Video Compression

It's usually preferable to keep file sizes as small as possible, but there's more to video compression than that. 

Here are the three primary advantages of video compression. 

  1. Requires less storage space: Video files that have been compressed are smaller and lighter in size. Video codecs such as H.264/AVC and H.265/HEVC, for example, can decrease original raw video data by up to 1,000 times. This means that raw video material of terabytes or gigabytes can be reduced into megabytes.
  1. Faster reading/writing of files: Because the movies are smaller in size and demand less bandwidth, video hosting services can process them quickly and simply (i.e., convenient uploads plus faster and smoother transmissions). This keeps the video from taking forever to load or crashing while playing. As a result, even consumers with slow internet connections may enjoy trouble-free video quality.
  1. Increased file transmission speed: The faster the transfer, the smaller the file size. Assume your device's file transmission speed is consistent at 100 Mbps. Keeping this in mind, moving 500 GB of raw video footage from one hard disc to another would take around an hour and a half. Assume the compression codecs employed reduced the data to 5 times their original size, or 100 GB. This means that the transfer will now take 18 minutes rather than 90 minutes.


According to the findings, CNN is a commonly utilized image or video compression technology. And apart from CNN, Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN) both can be utilized for this purpose. Autoencoders (AE) are also recommended for compression.

With all of the advantages that Deep Learning has to offer, namely in the video compression process. There are also various obstacles, such as the encoder search problem, reduced resolution, compression efficiency, support for new formats, more sophisticated compression, and time efficiency. However, with time and additional study, these issues may be rectified, and we may be able to accomplish much more in the field of video compression.

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