WiMi Hologram Cloud Develops A Digital Content Compression and Processing System for Web 3.0

WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (AR) Technology provider, announced that it has been continuously optimizing its digital content compression technology. It has launched a digital content compression processing system to accommodate Web 3.0’s high bit-rate transmission requirements.

Compression is the reduction of the amount of data needed to represent digital content. WiMi’s digital content compression and processing system mainly deal with four kinds of redundancy: coding redundancy, spatial redundancy, temporal redundancy, and redundancy of irrelevant information.

The redundancy of digital content data is mainly manifested as coding redundancy caused by the word code in the digital content being more prominent than the optimal coding to form entropy; spatial redundancy caused by the correlation between adjacent pixels in the digital content; temporal redundancy caused by the correlation between different frames in the digital content sequence; and spectral redundancy caused by the correlation between different color or spectrum bands. Due to the sheer volume of digital content data, which is very difficult to store, transmit, and process, the application of WiMi’s system is essential for the more efficient, intelligent, and realistic environment required by Web 3.0.

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Coding redundancy exists when the word code used is larger than the optimal code or relatively larger than the minimum length. This is where the concept of entropy comes into play, which has a more specific definition derived from a different discipline, entropy, in digital content processing. WiMi, therefore, optimizes codes intelligently by comparing them with particular algorithms and sorting out the disorganized codes to reduce the codes’ total entropy and redundancy.

Spatial redundancy is caused when addressing correlations between, for example, neighboring pixels in digital content. Spatial redundancy is a frequent type of data and is the most significant type presented in digital content images. There is often a spatial correlation between the colors of sampled points on the surface of the same scene, with adjacent points often taking on similar or identical values. Different data can have roughly the same histogram and entropy and approximately the same compression ratio. The pixels of any one image can reasonably be predicted from their neighboring pixel values, and these correlations are the potential basis for inter-pixel redundancy. To reduce inter-pixel redundancy, two-dimensional arrays of pixels can be transformed into a more efficient format. This type of transformation, known as mapping, takes the original digital content image data, transforms it into a dataset for reconstruction, and then merges it. The system will automatically identify and integrate to significantly reduce the amount of data in the digital content due to spatial redundancy and remove the excess data footprint.

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Temporal redundancy, in close analogy to spatial redundancy, arises because of the inter-pixel correlation of adjacent frames in digital content data. The system can insert successive frames of digital content into a matrix of frame structures, linking each frame along a four-dimensional array. The first two dimensions are the number of rows and columns dimensions, the third dimension is the monochrome image, and the fourth is the number of frames in the image sequence. Of course, temporal redundancy refers not only to the image data of digital content but also to data such as speech data, control data, and operational and informational data, all of which can be compiled using the same theoretical basis for integration.

Unlike coding and spatial redundancy, Irrelevant information is a way of processing digital content data using biases or insensitivity in human vision or perception. For example, the human eye is insensitive to high-frequency information in color, so irreversible quantitative compression can be performed.

WiMi’s digital content compression processing system is based on the basic principles of a lossless compression framework. The size of the digital content data is actually information plus data redundancy. When the fundamental problem of data redundancy and data results is optimized, the performance of transmission speed can be significantly improved. WiMi is also continuously optimizing its holographic digital content compression and processing system and has previously introduced a parallel compression scheme with multi-tasking packages to considerably reduce the processing time and improve its performance. WiMi will continue to improve the system’s intelligent processing capabilities and project management performance to provide better services to customers in the Web 3.0 era.

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