Large-size image data interaction method and device, medium and readable storage medium

Abstract

The invention discloses a large-size image data interaction method and device, a medium and a readable storage medium, and effectively realizes efficient storage and reading of image data by responding to storage and reading instructions. Firstly, a preset resolution set is traversed in sequence, layer-by-layer resampling is carried out, layered image data corresponding to all resolutions are generated, it is guaranteed that the data can be flexibly accessed under different resolutions, and efficient compression and storage of the high-resolution image data are provided. And then, based on a preset paging storage algorithm, writing the layered image data into different storage pages in sequence. In the reading process, the to-be-displayed area is determined by responding to the reading instruction, and the target resolution is calculated, so that the most appropriate resolution is selected for reading according to the specific requirement, the appropriate data paging can be automatically selected according to the display requirement in the reading process through the flexible resolution matching mode, and the data reading efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data storage technology, and in particular to a method, apparatus, medium, and readable storage medium for large-size image data interaction. Background Technology

[0002] Currently, with the increasing range of UAVs and the continuous advancement of remote sensing imaging technology, increasingly larger survey areas are being covered, resulting in massive amounts of orthophoto data, ranging from billions to tens of billions of pixels. Against this backdrop, how to efficiently store this large-size image data has become one of the key challenges in remote sensing data processing. Simultaneously, the economy of data storage, ease of transmission, and speed of read / write operations are crucial for improving data processing efficiency. First, the economy of data storage is mainly reflected in the ratio of source data to the actual size of the stored file. A higher compression ratio can effectively reduce disk usage and save storage resources, which is especially important in large-scale data storage scenarios. Second, ease of transmission depends on the choice of storage format. A single file format is easier for data migration and network transmission than multiple file storage formats, especially in the case of large data volumes, where reducing the number of files can significantly reduce transmission complexity and time costs. Finally, speed of read / write operations determines the efficiency of data storage and retrieval. Especially in practical applications where rapid reading and updating of image data is required, the design of the storage structure and the choice of compression algorithms directly affect the response speed of data processing. Therefore, optimizing storage methods can not only improve data storage efficiency, but also enable more efficient data transmission and utilization in subsequent processing, making it one of the core elements for large-scale UAV image data applications.

[0003] Existing methods for storing large-size image data typically rely on multi-file storage, with data distributed across multiple files. This increases complexity and latency during large-scale data transfer and migration, making it difficult to meet the demands for efficient and convenient transmission. Secondly, existing storage solutions often employ a uniform lossless or lossy compression strategy. While this ensures a certain level of image quality, it fails to fully consider the different quality and storage space requirements of image data at different resolutions. For example, raw-size images usually require lossless compression to preserve accurate image details, while lower-resolution sampled data may not necessarily need to be completely lossless, leading to wasted storage space. Summary of the Invention

[0004] This invention provides a method, apparatus, medium, and readable storage medium for large-size image data interaction, so as to reduce space waste in large-size image data storage and improve transmission efficiency and read / write speed.

[0005] To address the aforementioned technical problems, this invention provides a method for large-size image data interaction, comprising: In response to a storage command, the system acquires the original image data based on the storage command, sequentially traverses a preset set of resolutions, and resamples the original image data based on the current resolution to generate layered image data corresponding to each resolution. The layered image data are sorted in ascending order based on resolution to generate a layered image dataset. Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to different storage pages to generate storage files; In response to a read command, the system determines the area to be displayed and its size based on the read command, and calculates the target resolution based on the area to be displayed and its size. Based on the target resolution, the target storage page is matched in the storage file, and a target data block set is determined based on the target resolution and the target storage page. The target data block set is then resampled to obtain the target data.

[0006] This invention effectively achieves efficient storage and retrieval of image data by responding to storage and retrieval commands. First, a preset resolution set is sequentially traversed and resampled layer by layer to generate layered image data corresponding to each resolution. This ensures flexible access to data at different resolutions and provides efficient compression and storage of high-resolution image data. By sorting the layered image data in ascending order based on resolution, an ordered layered image dataset is generated, making subsequent paging storage more efficient and orderly, reducing redundancy in data storage. Furthermore, based on a preset paging storage algorithm, the layered image data is sequentially written to different storage pages, optimizing storage space utilization and improving data retrieval speed. During the retrieval process, the area to be displayed is determined by responding to retrieval commands, and the target resolution is calculated. The most suitable resolution is then selected for retrieval based on specific needs. This flexible resolution matching method allows for automatic selection of appropriate data pages based on display requirements during retrieval, reducing unnecessary data loading and improving data retrieval efficiency. Furthermore, by resampling the target data block set, the final displayed data is ensured to have good image quality and accurately match the target resolution. This not only improves the efficiency of storing and retrieving large-size image data, but also ensures high-quality image display, adapting to the accuracy and performance requirements in different application scenarios.

[0007] Furthermore, the response storage instruction, based on the storage instruction, acquires the original image data, sequentially traverses a preset resolution set, and resamples the original image data based on the current resolution to generate layered image data corresponding to each resolution, including: In response to a storage command, the original image data is acquired based on the storage command. The original image data is used as input data in the first resampling process; The preset resolution set is traversed sequentially, and the input data is resampled based on the current resolution to generate layered image data corresponding to each resolution; the resolution set includes several resolutions that decrease progressively from the original resolution. In each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

[0008] This invention effectively ensures the efficiency and consistency of image data generation at different resolutions by traversing a preset set of resolutions and resampling each resolution layer. During each resampling process, the image data is divided into blocks, and each data block is compressed, thereby reducing the space requirements for data storage while maintaining image quality. The layered image data generated in this way is more flexible in storage and transmission, meeting different display needs.

[0009] Furthermore, in each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. This layered image data is then used as the input data for the next resampling process, including: If the current sampling process is the first resampling, the input data is divided into several data blocks based on a preset size, and each data block is losslessly compressed to generate layered image data corresponding to the original resolution. The layered image data is then used as the input data for the next resampling process. If not, the input data is divided into several data blocks based on a preset size, and each data block is subjected to lossy compression to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

[0010] This invention optimizes data storage and processing efficiency by differentiating the compression methods used for data blocks in different resampling stages. During the first resampling, lossless compression is employed to preserve the accuracy of the image data, while lossy compression is used in subsequent resampling processes to further reduce the data volume. This flexible compression strategy effectively reduces storage space usage while improving image data processing efficiency and transmission performance, adapting to application scenarios with varying accuracy and data volume requirements.

[0011] Furthermore, the layered image data in the layered image dataset is sequentially written to different storage pages based on a preset paging storage algorithm to generate a storage file, including: Based on the quantity of the layered image data, several storage pages are constructed and the storage pages are initialized; Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to each storage page to generate a storage file; wherein each storage page corresponds to one layered image data.

[0012] This invention utilizes a paging storage algorithm to sequentially write layered image data into different storage pages. By initializing and optimizing these pages, the storage process becomes more efficient and orderly. Paging storage allows image data to be read on demand, avoiding unnecessary resource waste caused by loading the entire dataset. Furthermore, paging storage effectively reduces storage space usage while ensuring fast data access, improving the overall performance and scalability of the storage system.

[0013] Furthermore, the step of matching target storage pages in the storage file based on the target resolution, determining a target data block set based on the target resolution and the target storage pages, and resampling the target data block set to obtain target data includes: Match the target storage page in the storage file based on the target resolution; In the target storage paging, the set of target data blocks to be read is determined based on the target resolution; Based on the target data block set, the corresponding target data blocks are extracted from the target storage page, and the target data blocks are resampled to obtain resampled data; The resampled data is stitched together based on spatial location to generate target data.

[0014] This invention, based on the target resolution, matches target storage pages in the storage file and resamples data blocks, effectively improving the accuracy and efficiency of data reading and display. It ensures that the read data blocks are accurately resampled at the target resolution, and generates target data through stitching, further improving the accuracy and smoothness of image display. The on-demand reading mechanism reduces unnecessary storage operations and data loading, significantly optimizing the reading and display speed of large-size image data.

[0015] Furthermore, it also includes: In response to a data modification instruction, obtain the data to be modified and the corresponding range to be modified based on the data modification instruction; Based on the range to be modified, match the data blocks to be modified corresponding to each storage page in the storage file; The data block to be modified is resampled and compressed to obtain the data to be updated corresponding to each storage page; The data to be updated is written to the corresponding storage page to complete the data modification.

[0016] This invention optimizes the data update process by precisely matching the data blocks to be modified and performing resampling and compression. During data modification, resampling ensures the accuracy of the updated data, while compression reduces storage space requirements. This not only improves modification efficiency but also ensures the consistency and accuracy of the modified data in storage. Furthermore, the efficient storage and update mechanism during data modification helps improve the system's real-time performance and response speed, adapting to the dynamic update needs of large-scale image data.

[0017] In a second aspect, the present invention provides a communication device including a module for performing the method.

[0018] Thirdly, the present invention provides a communication device, including a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices and transmit them to the processor or to send signals from the processor to other communication devices, and the processor is used to implement the method through logic circuits or execution code instructions.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing a computer program or instructions that, when executed by a communication device, implement the method described therein.

[0020] Fifthly, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implement the method described therein. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a large-size image data interaction method provided in an embodiment of the present invention; Figure 2 A schematic diagram of a data resampling process provided in an embodiment of the present invention; Figure 3 A schematic diagram of a data segmentation process provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of another data resampling process provided in an embodiment of the present invention; Figure 5 A schematic diagram of a file storage structure provided in an embodiment of the present invention; Figure 6 A schematic diagram of a data reading process provided in an embodiment of the present invention; Figure 7 A schematic diagram of a data modification process provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of a sampling interpolation refresh pagination process provided by an embodiment of the present invention; Figure 9 This is a schematic diagram of a data update process provided in an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] The terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] Example 1 See Figure 1 , Figure 1 This is a flowchart illustrating a large-size image data interaction method provided by an embodiment of the present invention. The embodiment of the present invention provides a large-size image data interaction method, including steps 101 to 105, as detailed below: Step 101: Respond to the storage instruction, obtain the original image data based on the storage instruction, traverse the preset resolution set in sequence, and resample the original image data based on the current resolution to generate layered image data corresponding to each resolution; In this embodiment, the response storage instruction, based on the storage instruction, acquires the original image data, sequentially traverses a preset resolution set, and resamples the original image data based on the current resolution to generate layered image data corresponding to each resolution, including: In response to a storage command, the original image data is acquired based on the storage command. The original image data is used as input data in the first resampling process; The preset resolution set is traversed sequentially, and the input data is resampled based on the current resolution to generate layered image data corresponding to each resolution; the resolution set includes several resolutions that decrease progressively from the original resolution. In each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

[0026] In this embodiment, firstly, upon receiving a storage instruction, the system acquires the original image data based on the instruction. This original image data serves as the input data for the first resampling process. Next, the system resamples the original image data sequentially according to a preset resolution set, generating layered image data at different resolutions. The resolution set includes multiple resolutions that decrease progressively from the original resolution, ensuring that each resampling step generates image data at different scales to accommodate varying storage and retrieval requirements.

[0027] In this embodiment, during each resampling process, the system divides the current input data into multiple data blocks according to a preset size, with each data block representing a small region of the image. These data blocks are compressed individually to reduce storage space usage. The compressed data blocks form layered image data at the current resolution and are used as input data for the next resampling process. This block-based and compression process ensures efficient storage of image data while providing greater flexibility and access speed for subsequent retrieval.

[0028] In this embodiment, during each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. This layered image data is then used as the input data for the next resampling process. If the current sampling process is the first resampling, the input data is divided into several data blocks based on a preset size, and each data block is losslessly compressed to generate layered image data corresponding to the original resolution. The layered image data is then used as the input data for the next resampling process. If not, the input data is divided into several data blocks based on a preset size, and each data block is subjected to lossy compression to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

[0029] In this embodiment, data for each resolution layer is segmented and compressed to optimize data storage space and ensure efficient retrieval. During each resampling process, the input data is divided into several small data blocks based on a preset size, with each data block representing a portion of the image. These data blocks undergo independent compression processing to generate layered image data for the corresponding resolution layer, thereby providing an efficient data structure for subsequent storage and access.

[0030] In this embodiment, lossless compression is used when processing the original image data, i.e., during the first resampling process. At this time, the system divides the original image data into several data blocks and performs lossless compression on each data block separately. Lossless compression ensures accurate preservation of the image data and is particularly suitable for situations where data integrity must be guaranteed, such as saving data at its original resolution. The layered image data generated in this process will be used as input data for the next resampling process, continuing the layer-by-layer resolution reduction process.

[0031] In this embodiment, during subsequent resampling processes, when processing lower-resolution data, the system employs a lossy compression method. This method effectively reduces the data volume and adapts to different storage requirements. In this process, the input data is still divided into several data blocks according to a preset size, and each data block is still compressed independently, but lossy compression technology is used, which can significantly reduce the data storage space occupied while maintaining a certain image quality. These compressed data blocks will continue to generate layered image data at the current resolution and serve as input data for the next round of resampling.

[0032] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a data resampling process provided in an embodiment of the present invention.

[0033] In this embodiment, the size of the original image data is In the resampling process, the resolution of the current sample is first determined, and resampling is performed based on this resolution. In each resampling process, the input data is divided into several blocks based on a preset size. Taking a resolution of 128x128 for each block as an example.

[0034] Please refer to Figure 3 and Figure 4 , Figure 3 This is a schematic diagram of a data segmentation process provided in an embodiment of the present invention. Figure 4 This is another schematic diagram of the data resampling process provided in an embodiment of the present invention.

[0035] In this embodiment, each data block serves as the smallest storage unit, capable of being processed independently during subsequent storage and retrieval processes. Next, during the first resampling process, the image resolution is the original resolution. Therefore, the system uses the LZW lossless compression algorithm to compress each block, ensuring that the data is not distorted after compression, thereby preserving the details and quality of the original image to the maximum extent. These compressed data blocks form layered image data corresponding to the current resolution and serve as input data for the next resampling process, further generating a lower-resolution data layer.

[0036] In subsequent resampling, the system continues to divide the input data into several data blocks and compresses each data block using the JPEG lossy compression algorithm. Compared with LZW lossless compression, the JPEG lossy compression algorithm can significantly reduce data storage space and is suitable for processing low-resolution layer data. Especially when storing large-size images, by appropriately reducing image quality, it can effectively save storage space and improve data access efficiency.

[0037] In this embodiment, when the image data is large, the disk read / write efficiency has a significant impact on the compression speed. During compression, the uncompressed original image data needs to be read first, which typically takes a considerable amount of time. The compressed data is relatively smaller than the original data, so the writing time is less noticeable. During decompression, only the compressed data needs to be read, so compression algorithms with higher compression ratios are more advantageous.

[0038] In this embodiment, the following table shows the comparison of compression rate, read rate and storage economy of different levels of Sample 1 and Sample 2 using LZW and JPEG (quality factor 70), respectively. Table 1 is a schematic table of sample 1 layer by layer sampling, and Table 2 is a schematic table of sample 2 layer by layer sampling.

[0039] Table 1 Table 2 In this embodiment, large images are faster to compress using LZW, although the time complexity of both LZW and JPEG algorithms is... However, when the data volume is large, JPEG requires DCT transformation because it involves... The cosine calculation is performed repeatedly, which is time-consuming. LZW, by building a dictionary with a large amount of data, increases the hit rate of data within the dictionary, thus improving compression efficiency. Therefore, when the image data size is large, LZW is faster than JPEG in compression. JPEG has a higher compression ratio because it quantizes the DCT results during compression, ignoring high-frequency data that is not sensitive to the human eye, resulting in a better compression ratio. LZW's dictionary matching mode only achieves a good compression ratio for images with a large amount of repetitive data. Since image signals usually have random color value variations, its compression ratio is often lower than JPEG.

[0040] Although the JPEG algorithm is slightly slower than LZW, thanks to JPEG's higher compression ratio, it can save a lot of read I / O during decompression, which makes up for the disadvantage of the more complex algorithm. Therefore, when the amount of data to be read is small, the speed of JPEG and LZW algorithms is similar, while when the amount of data to be read is large, the JPEG algorithm is faster.

[0041] Therefore, since the original image data is relatively large, using LZW ensures no data loss and provides a good compression ratio with a short compression time. Other layered data, on the other hand, are smaller and essentially redundant backups of the original image. These layers are generated from samples of the original image data and already suffer from data loss; using JPEG achieves a better compression ratio for them. Furthermore, layered data exists to accelerate subsequent reading, and JPEG speeds up decompression. While reading original-size data using LZW compression is slower than JPEG, full decompression of original-size data is rare. When original-size data needs to be read, it usually means the display resolution is greater than or equal to the photo resolution. Since display sizes are mostly 2000x1000 pixels, due to the use of block storage, only a small portion of the original image size needs to be read, so the reading speed is not significantly different.

[0042] Through this process, the layered image data generated by the system can be optimized for storage according to different resolution layers, ensuring the accuracy of the original image while maximizing the utilization of storage space. Furthermore, block-based and compression processing ensures that data can be loaded quickly and read efficiently at different resolutions, meeting the high-efficiency storage and retrieval needs of large-size image data in various applications.

[0043] In this embodiment, the accuracy of the original image data is maintained while storage requirements are effectively reduced through lossy compression during lower-resolution processing. This block compression strategy not only optimizes storage space but also provides a flexible data access structure, meeting the dual requirements of storage efficiency and data quality in different application scenarios.

[0044] Step 102: Sort the layered image data in ascending order based on resolution to generate a layered image dataset; In this embodiment, during the storage and processing of large-size image data, to efficiently manage image data at different resolutions, the generated layered image data first needs to be sorted. Specifically, after resampling each resolution layer, the image data is sorted according to the resolution of each layer, ensuring that the resolution decreases from high to low, thus forming a sequentially arranged layered image dataset. In this process, firstly, the system identifies all resampled layered image data and extracts the resolution corresponding to each layer. Then, these image data are sorted in ascending order according to their resolution, starting with the image data with the highest resolution and proceeding sequentially until the layer with the lowest resolution. The sorted layered image dataset is stored in this order, preparing for subsequent paging storage.

[0045] In this embodiment, sorting the layered image dataset not only ensures that the data is stored in descending order of resolution, but also optimizes the data access structure. This ordered arrangement allows for more efficient paging and retrieval processes, avoiding unnecessary data redundancy and access delays, while also providing more efficient support for on-demand reading and display.

[0046] Step 103: Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to different storage pages to generate a storage file; Please refer to Figure 5 , Figure 5 This is a schematic diagram of a file storage structure provided in an embodiment of the present invention.

[0047] In this embodiment, the original image data is first layered. Layering aims to divide large-size image data into different resolution layers for easier subsequent storage and retrieval. Next, the system executes the steps shown in the diagram sequentially. First, the image data is sorted, prioritizing sorting by layer or resolution for more efficient storage. Then, initial files are created based on the different resolution sizes of the layered images. At this stage, the system generates an initial file container to store the original-size image data. This file container contains the original layers of the image data, providing the basic storage structure for subsequent data processing. Subsequently, the system continues to write data from different resolution layers into the layered storage according to a preset layering strategy, with each storage page corresponding to a layer of image data at one resolution.

[0048] In this embodiment, efficient storage and retrieval of image data are ensured through a layered and progressive writing approach. During storage, the layered data processing method effectively reduces storage space usage while improving image data retrieval efficiency. Especially when dealing with large-size image data, layered storage can flexibly adjust the resolution level of data retrieval according to specific needs, thereby improving the overall system performance.

[0049] In this embodiment, the method of sequentially writing the layered image data in the layered image dataset to different storage pages based on a preset paging storage algorithm to generate a storage file includes: Based on the quantity of the layered image data, several storage pages are constructed and the storage pages are initialized; Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to each storage page to generate a storage file; wherein each storage page corresponds to one layered image data.

[0050] In this embodiment, firstly, several storage pages are constructed based on the amount of layered image data. Each page will contain a certain amount of image data, and the size and number of each page are determined according to the resolution and data volume of the image data.

[0051] In this embodiment, the core structural unit of the stored file is a "storage page," and each storage page independently stores complete image data at a specific resolution or sampling level. For example... Figure 5 As shown on the left, the beginning of each page is the metadata area, which defines the basic attributes of this layer of imagery, such as length, width, pixel bit depth, color order, and the compression algorithm used. A key component of the metadata is the block index table, which records the "offset address" and "length" of each data block in the file. Figure 5 As shown in the middle section, these index entries, through explicit pointer relationships, point to the contiguous, compressed actual data blocks stored on the right. This separation of "metadata-data block" structure allows the system to accurately locate and read any image region without sequential scanning. Specifically, the metadata for each page also contains a crucial "page offset" pointer, which directly indicates the starting position of the next-level (usually lower resolution) page in the file. Thus, all pages form a unidirectional chain through this pointer, achieving sequential logical association and efficient physical addressing from the highest resolution layer to the lowest resolution layer.

[0052] In this embodiment, the metadata of the storage page includes: basic attributes of the image on this page; the identifier of the compression algorithm used on this page; the offset address and length of one or more data blocks within this page, each data block storing compressed data of a portion of the image on this page; and a page offset address pointing to the starting position of the storage page to which the next sampling level belongs; wherein, the image data of the current sampling level is divided into one or more data blocks according to the metadata, and each data block is compressed according to the compression algorithm identifier, and the compressed data is continuously written to the storage file according to the offset address and length of each data block recorded in the metadata; wherein, the page offset address is used to chain-locate and jump to the storage page corresponding to the next sampling level during reading.

[0053] In this embodiment, after constructing the storage pages, each storage page is initialized to prepare for storing image data. The initialization process includes allocating appropriate storage space for each page and creating necessary indexes and metadata records for subsequent data writing, ensuring the stability and scalability of the storage page structure and providing a foundation for efficient data access. Then, based on a preset paging storage algorithm and according to the previous sorting results, each layer of image data in the layered image dataset is sequentially written to its corresponding storage page. Each layer of image data is precisely written to the storage page corresponding to its resolution, ensuring that data of different resolutions can be stored independently and accessed on demand. This paging storage method ensures that each storage page contains only image data of a specific resolution layer, avoiding the complexity caused by mixed storage of data of different resolutions and simplifying the data reading process. Finally, a large-size image data storage file containing multiple storage pages is generated, ensuring efficient data storage, convenient retrieval, and excellent storage space utilization. Through paging storage, the system can quickly locate the image data of the specific resolution layer that needs to be read, thereby significantly improving data reading speed and reducing unnecessary storage and access overhead, making it particularly suitable for the management and processing of large-scale image data.

[0054] Step 104: Respond to the read command, determine the area to be displayed and the area size based on the read command, and calculate the target resolution based on the area to be displayed and the area size; In this embodiment, upon receiving a read command, the system extracts the location information and size of the area to be displayed contained in the command. The location information includes the starting coordinates of the area, and the area size includes the width and height of the area. This information is typically provided by the user or application, indicating the specific area of ​​the image that the user wishes to display. Next, the system calculates a target resolution based on the size of the area to be displayed and the device's display capabilities, which include the screen's resolution and physical dimensions.

[0055] In this embodiment, the target resolution is calculated based on the adaptation relationship between the area to be displayed and the display device. Specifically, the system compares the size of the area to be displayed with the size of the display device to determine an optimal scaling ratio, which determines the resolution to which the image data needs to be scaled. Typically, the target resolution is the optimal value of the scaling ratio of the area to be displayed relative to the device's display area, ensuring that the image content in the display area is neither distorted nor out of sync with the device's display capabilities.

[0056] Step 105: Match the target storage page in the storage file based on the target resolution, determine the target data block set based on the target resolution and the target storage page, and resample the target data block set to obtain the target data.

[0057] In this embodiment, the step of matching target storage pages in the storage file based on the target resolution, determining a target data block set based on the target resolution and the target storage pages, and resampling the target data block set to obtain target data includes: Match the target storage page in the storage file based on the target resolution; In the target storage paging, the set of target data blocks to be read is determined based on the target resolution; Based on the target data block set, the corresponding target data blocks are extracted from the target storage page, and the target data blocks are resampled to obtain resampled data; The resampled data is stitched together based on spatial location to generate target data.

[0058] First, upon receiving a read command, the system calculates the display resolution by determining the coordinates and dimensions of the area to be displayed. The display resolution is determined by the ratio of the area to the display device, and the system selects the closest storage page based on this resolution. This process ensures that the data read from the storage file matches the target resolution, avoiding the reading of unnecessary data layers.

[0059] Please refer to Figure 6 , Figure 6 This is a schematic diagram of a data reading process provided in an embodiment of the present invention.

[0060] In this embodiment, during the reading and display of large-size image data, to improve reading efficiency and image quality, the system needs to select an appropriate resolution based on user requirements or device capabilities and accurately locate the required image data in the storage file. First, upon receiving a read command, the system finds the storage page in the storage file that best matches the target resolution. Each page corresponds to data at a specific resolution layer; therefore, the system selects the page closest to the target resolution for reading. This process ensures that the read data meets the target resolution requirements while avoiding reading too much unnecessary resolution layer data, thus improving efficiency. Next, based on the target resolution and the target storage page, the system determines the set of target data blocks to be read. A data block is the smallest unit in storage, and each data block represents a portion of the image area. The system calculates the area range of the specific data blocks to be read based on the target resolution and ensures that the read area covers the entire display area. Through precise positioning and selection, the system reads only the necessary data, avoiding resource waste caused by over-reading. Subsequently, the system extracts the corresponding target data blocks from the target storage page and resamples each data block. The resampling operation adjusts the resolution of data blocks to the target resolution, ensuring that the read data accurately matches the display requirements. Common resampling methods include bilinear interpolation, which guarantees the image quality after scaling. Finally, the resampling data blocks are stitched together according to their spatial location to generate the target data. The stitching operation ensures the spatial continuity of each data block, allowing the final target data to be seamlessly connected, presenting complete image content for display or further processing.

[0061] In this embodiment, during the reading process, the system first determines the coordinates and size of the area to be displayed based on the reading instruction. And calculate the target resolution of the display area. The formula for calculating the display resolution s is: (1) in, and It refers to the size of the device's display area. and This is the original size of the area to be displayed. Through this calculation, the target resolution of the display area is determined and compared with the resolutions in the storage pages (e.g., 1, 2, 4, 8, etc.). The page closest to the target resolution is selected for reading.

[0062] In this embodiment, the data block is further searched in the storage file. Assuming the resolution of the current page is r and the original size of the image is (w, h), the data block number τ to be read is: (2) (3) (4) Where x and y are the coordinates of the top left corner of the display area, respectively; and is the size of the area to be displayed; w is the original width of the image, i.e., the full width of the image; h is the original height of the image (in pixels), i.e., the full height of the image; n is the offset used to represent the column when the data is divided into blocks, and is an integer value; m is the offset used to represent the row when the data is divided into blocks, and is an integer value; τ is the data block number, indicating the position of the data block to be read in the storage page.

[0063] In this embodiment, by combining the coordinates of the area to be displayed with the resolution, the corresponding row and column positions (col and row) in the current page are calculated, and the data block number τ to be read is finally obtained.

[0064] In this embodiment, to facilitate subsequent resampling, the system expands the reading area by subtracting the resolution r from the x and y coordinates and increasing the resolution r by twice in the w and h coordinates, ensuring that the reading range expands by one pixel from the current page resolution, thereby covering possible boundary areas.

[0065] In this embodiment, based on the calculated block number, the system reads each data block in parallel from the storage pages. This parallel reading strategy can significantly improve data reading efficiency, especially when processing large-size image data, and can significantly reduce reading time. Finally, in the resampling step, the system stitches the read block data and interpolates the data according to the target resolution. Through this resampling process, the system generates image region data that conforms to the target resolution, preparing for final display or further processing.

[0066] In this embodiment, as mentioned earlier, when the original image data is large, disk read / write I / O will significantly impact the compression speed. If each stage samples from the original image data, it will result in repeated read I / O, increasing the compression process time. However, by using a step-by-step sampling method, data read I / O can be greatly reduced, thereby speeding up the compression process.

[0067] Table 3 In this embodiment, it also includes: In response to a data modification instruction, obtain the data to be modified and the corresponding range to be modified based on the data modification instruction; Based on the range to be modified, match the data blocks to be modified corresponding to each storage page in the storage file; The data block to be modified is resampled and compressed to obtain the data to be updated corresponding to each storage page; The data to be updated is written to the corresponding storage page to complete the data modification.

[0068] Please refer to Figure 7 and Figure 8 , Figure 7 A schematic diagram of a data modification process provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of a sampling interpolation refresh pagination process provided in an embodiment of the present invention.

[0069] In this embodiment, in response to a data modification command, the system searches for blocks in the storage file. By searching for blocks in each relevant storage page, it ensures that the data block requiring update is accurately located. Next, dual-thread interpolation sampling is performed on each found data block to adjust the data's precision and quality, ensuring data matching at the target resolution. At this point, the interpolated data is recompressed using an appropriate compression algorithm to reduce storage space usage and optimize subsequent read and transmission efficiency. After data compression, the compressed data is directly written to the original storage location, ensuring data integrity and consistency. Based on this, the length record of the current data block is updated to ensure the accuracy of the data block's storage information. If, during this process, the updated data size is found to be larger than the original data, the new compressed data is appended to the end of the file to avoid data loss due to overwriting. Subsequently, the offset information of the appended data is updated to ensure the integrity and traceability of the file structure.

[0070] Finally, the system checks whether all blocks have been updated. If all blocks have been updated, the system will end the process. If some blocks have not yet been updated, the system will continue to perform the update operation until all blocks and page data have been updated.

[0071] Please refer to Figure 9 , Figure 9 This is a schematic diagram of a data update process provided in an embodiment of the present invention.

[0072] In this embodiment, the reading and storage process of large-size image data is optimized by reusing storage addresses, thereby avoiding the shifting operations that occur when inserting data in single-file storage and accelerating the data writing speed. In traditional storage schemes, when data needs to be inserted, it often triggers subsequent data shifting operations, slowing down the writing process. To improve writing efficiency, this scheme adopts a strategy of reusing storage addresses, directly writing new data to reserved storage space without moving existing data. The storage paging structure is organized into multiple file parts, including a file header, a file middle section, and a paging section. The file header contains basic information about the compressed data, the file middle section stores the actual compressed data, and the paging section stores the data structure and corresponding block information of each block. The data in each page includes information such as the block length and offset, and the data within the page and the data in the file middle section can be closely combined. When data is updated or added, a block backup is first performed at a preset storage address, and the updated block data is directly written to the specified storage location without moving other data. In this way, the system ensures the integrity of the data storage structure by updating the block length and offset information. Whenever new data is appended, the system reuses existing storage addresses and maintains data order by updating relevant block information (such as the lengths of block 1 and block 2), avoiding the data migration required during traditional writes. This significantly improves data writing efficiency, especially when processing large-scale data, avoiding the performance bottleneck of insertion operations. Simultaneously, the strategy of reusing storage addresses also increases storage space utilization, effectively reducing space waste. Therefore, this solution significantly accelerates data writing speed and improves overall system performance while ensuring the stability of the data structure during data storage and updates.

[0073] This invention optimizes the data update process by precisely matching the data blocks to be modified and performing resampling and compression. During data modification, resampling ensures the accuracy of the updated data, while compression reduces storage space requirements. This not only improves modification efficiency but also ensures the consistency and accuracy of the modified data in storage. Furthermore, the efficient storage and update mechanism during data modification helps improve the system's real-time performance and response speed, adapting to the dynamic update needs of large-scale image data.

[0074] In this embodiment, efficient storage and retrieval of image data are effectively achieved by responding to storage and retrieval commands. First, a preset resolution set is sequentially traversed and resampled layer by layer to generate layered image data corresponding to each resolution. This ensures flexible access to data at different resolutions and provides efficient compression and storage of high-resolution image data. By sorting the layered image data in ascending order based on resolution, an ordered layered image dataset is generated, making subsequent paging storage more efficient and orderly, reducing redundancy in data storage. Furthermore, based on a preset paging storage algorithm, the layered image data is sequentially written to different storage pages, optimizing storage space utilization and improving data retrieval speed. During the retrieval process, the area to be displayed is determined and the target resolution is calculated by responding to retrieval commands. The most suitable resolution is then selected for retrieval based on specific needs. This flexible resolution matching method allows for automatic selection of appropriate data pages based on display requirements during retrieval, reducing unnecessary data loading and improving data retrieval efficiency. Furthermore, by resampling the target data block set, the final displayed data is ensured to have good image quality and accurately match the target resolution. This not only improves the efficiency of storing and retrieving large-size image data, but also ensures high-quality image display, adapting to the accuracy and performance requirements in different application scenarios.

[0075] The present invention provides a communication device, including a module for performing the method.

[0076] The present invention provides a communication device, including a processor and an interface circuit. The interface circuit is used to receive signals from other communication devices and transmit them to the processor, or to send signals from the processor to other communication devices. The processor is used to implement the method through logic circuits or execution code instructions.

[0077] The present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implement the method described therein.

[0078] In this embodiment of the invention, a processing device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described large-size image data interaction method.

[0079] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the above-described large-size image data interaction method when it is running.

[0080] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a processor to perform the present invention. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a processing device.

[0081] The processing device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processing device may include, but is not limited to, a processor, memory, and a display. Those skilled in the art will understand that the above components are merely examples of the processing device and do not constitute a limitation on the processing device. It may include more or fewer components than the specified components, or a combination of certain components, or different components. For example, the processing device may also include input / output devices, network access devices, buses, etc.

[0082] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the processing device, connecting all parts of the processing device through various interfaces and lines.

[0083] Memory can be used to store computer programs and / or modules. The processor performs various functions of the processing device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function (such as sound playback, text conversion, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0084] In this invention, the module for interacting with large-size image data, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Those skilled in the art can understand and implement this invention without any inventive effort.

[0085] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for large-size image data interaction, characterized in that, include: In response to a storage command, the system acquires the original image data based on the storage command, sequentially traverses a preset set of resolutions, and resamples the original image data based on the current resolution to generate layered image data corresponding to each resolution. The layered image data are sorted in ascending order based on resolution to generate a layered image dataset. Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to different storage pages to generate storage files; In response to a read command, the system determines the area to be displayed and its size based on the read command, and calculates the target resolution based on the area to be displayed and its size. Based on the target resolution, the target storage page is matched in the storage file, and a target data block set is determined based on the target resolution and the target storage page. The target data block set is then resampled to obtain the target data.

2. The method for large-size image data interaction as described in claim 1, characterized in that, The response storage instruction retrieves the original image data based on the storage instruction, sequentially traverses a preset resolution set, and resamples the original image data based on the current resolution to generate layered image data corresponding to each resolution, including: In response to a storage command, the original image data is acquired based on the storage command. The original image data is used as input data in the first resampling process; The preset resolution set is traversed sequentially, and the input data is resampled based on the current resolution to generate layered image data corresponding to each resolution; the resolution set includes several resolutions that decrease progressively from the original resolution. In each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

3. The method for large-size image data interaction as described in claim 2, characterized in that, In each resampling process, the input data is divided into several data blocks based on a preset size, and each data block is compressed to generate layered image data corresponding to the current resolution. This layered image data is then used as the input data for the next resampling process. If the current sampling process is the first resampling, the input data is divided into several data blocks based on a preset size, and each data block is losslessly compressed to generate layered image data corresponding to the original resolution. The layered image data is then used as the input data for the next resampling process. If not, the input data is divided into several data blocks based on a preset size, and each data block is subjected to lossy compression to generate layered image data corresponding to the current resolution. The layered image data is then used as the input data for the next resampling process.

4. The method for large-size image data interaction as described in claim 3, characterized in that, The preset paging storage algorithm sequentially writes the layered image data in the layered image dataset into different storage pages to generate a storage file, including: Based on the quantity of the layered image data, several storage pages are constructed and the storage pages are initialized; Based on a preset paging storage algorithm, the layered image data in the layered image dataset is sequentially written to each storage page to generate a storage file; wherein each storage page corresponds to one layered image data.

5. The method for large-size image data interaction as described in claim 4, characterized in that, The process of matching target storage pages in the storage file based on the target resolution, determining a target data block set based on the target resolution and the target storage pages, and resampling the target data block set to obtain target data includes: Match the target storage page in the storage file based on the target resolution; In the target storage paging, the set of target data blocks to be read is determined based on the target resolution; Based on the target data block set, the corresponding target data blocks are extracted from the target storage page, and the target data blocks are resampled to obtain resampled data; The resampled data is stitched together based on spatial location to generate target data.

6. The method for large-size image data interaction as described in claim 5, characterized in that, Also includes: In response to a data modification instruction, obtain the data to be modified and the corresponding range to be modified based on the data modification instruction; Based on the range to be modified, match the data blocks to be modified corresponding to each storage page in the storage file; The data block to be modified is resampled and compressed to obtain the data to be updated corresponding to each storage page; The data to be updated is written to the corresponding storage page to complete the data modification.

7. A communication device, characterized in that, Includes a module for performing the method as described in any one of claims 1 to 6.

8. A communication device, characterized in that, The device includes a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices and transmit them to the processor or to send signals from the processor to other communication devices, and the processor is used to implement the method as described in any one of claims 1 to 6 through logic circuits or execution code instructions.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the method as described in any one of claims 1 to 6 is implemented.

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