Image compression performance optimization method and device based on EZW, equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of digital image compression, in particular to an EZW-based image compression performance optimization method and device, equipment, a storage medium and a computer program product. The method comprises the following steps of: decomposing image data into multi-level wavelet coefficients by carrying out wavelet transformation on the image data; performing regional gradient quantization processing on the multi-stage wavelet coefficient; based on an EZW algorithm, sequentially performing main scanning and auxiliary scanning on the multi-stage wavelet coefficient after the regional gradient quantization processing according to a sequence from high frequency to low frequency; in a main scanning process, dynamically judging whether a descendant node of a father node needs to be coded or not based on an important coefficient parasitic flag bit; and on the basis of the main scanning result, the auxiliary scanning result and the judgment result, coding data is generated and transmitted, and the image compression performance is improved.

Description

Technical Field

[0001] The present application relates to the field of digital image compression technology, and in particular to an EZW-based image compression performance optimization method, apparatus, device, storage medium, and computer program product. Background Art

[0002] With the rapid development of digital integrated circuits, the demand for image processing technology in mobile devices continues to grow. To improve the efficiency of image transmission and storage, image compression technology has been widely researched and applied. In particular, in scenarios requiring high compression ratios, lossy compression algorithms are widely adopted, significantly reducing data volume at the expense of image quality. Wavelet-based image compression algorithms can further optimize compression performance. The embedded zerotree sweep (EZW) algorithm further enhances the performance of wavelet transform algorithms. The EZW algorithm leverages the correlation between parent and child wavelet transform coefficients to achieve efficient image compression based on a zerotree structure. However, in certain scenarios, when a parent node is identified as an important coefficient, its descendant nodes are often redundantly encoded, reducing compression performance. Furthermore, the distribution characteristics of wavelet coefficients across different frequency regions are not fully utilized, resulting in low quantization efficiency and, consequently, poor image compression performance. Therefore, improving image compression performance has become a pressing technical challenge. Summary of the Invention

[0003] The main purpose of this application is to provide an EZW-based image compression performance optimization method, device, equipment, storage medium and computer program product, aiming to solve the technical problem of how to improve image compression performance.

[0004] To achieve the above objectives, the present application provides an EZW-based image compression performance optimization method, the method comprising the following steps:

[0005] Performing wavelet transform on the image data to decompose the image data into multi-level wavelet coefficients;

[0006] Performing regional gradient quantization processing on the multi-level wavelet coefficients;

[0007] Based on the EZW algorithm, the multi-level wavelet coefficients after regional gradient quantization are subjected to main scanning and auxiliary scanning in the order from high frequency to low frequency.

[0008] During the main scanning process, based on the parasitic flag of the important coefficient, dynamically determine whether the descendant nodes of the parent node need to be encoded;

[0009] Based on the main scanning result, the auxiliary scanning result and the judgment result, the encoded data is generated and transmitted.

[0010] In one embodiment, the step of performing wavelet transform on the image data to decompose the image data into multi-level wavelet coefficients includes:

[0011] Extracting frequency distribution characteristics of the image data, and determining a target wavelet basis function and a number of decomposition layers based on the frequency distribution characteristics;

[0012] Based on the target wavelet basis function and the number of decomposition layers, the image data is decomposed in a row direction and a column direction to obtain a plurality of sub-bands;

[0013] Performing the row-wise decomposition and the column-wise decomposition on the row and column low-frequency sub-bands in the plurality of sub-bands to obtain multi-level sub-bands;

[0014] The multi-level wavelet coefficients are determined according to the multiple sub-bands and the multi-level sub-bands.

[0015] In one embodiment, the step of performing region-by-region gradient quantization processing on the multi-level wavelet coefficients includes:

[0016] Dividing the multi-level wavelet coefficients into a plurality of frequency regions based on the sub-band frequencies and the number of decomposition levels of the multi-level wavelet coefficients;

[0017] determining a quantization factor allocation strategy according to characteristics of the image data in each frequency region;

[0018] Based on the quantization factor allocation strategy, quantization processing is performed on the sub-band coefficients corresponding to each frequency region.

[0019] In one embodiment, the step of performing main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm includes:

[0020] determining a scanning threshold according to the maximum absolute value of the multi-level wavelet coefficients after the sub-region gradient quantization processing;

[0021] Performing the main scan on the multi-level wavelet coefficients after the gradient quantization processing of the sub-regions in order from high frequency to low frequency, and comparing the main scan result with the scan threshold;

[0022] Based on the comparison result, generating an important coefficient sub-table, wherein the important coefficient sub-table includes important coefficients and positions of the important coefficients;

[0023] Based on the important coefficient sub-table, the auxiliary scanning is performed on the important coefficients.

[0024] In one embodiment, the step of performing the auxiliary scanning on the important coefficients based on the important coefficient sub-table includes:

[0025] Traversing the important coefficient sub-table, and performing refinement coding on the absolute values ​​of the important coefficients;

[0026] Determining whether the absolute value of the important coefficient is within a preset interval;

[0027] If so, based on the refined coding result, a corresponding binary coding sequence is generated, and the positive and negative information of the important coefficients are recorded.

[0028] In one embodiment, after the step of performing main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing based on the EZW algorithm in the order from high frequency to low frequency, the method further includes:

[0029] Based on a preset threshold reduction strategy, reducing the value of the scanning threshold;

[0030] The main scan and the auxiliary scan are repeatedly performed until the scan threshold decreases to a preset minimum threshold.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes an EZW-based image compression performance optimization device, the EZW-based image compression performance optimization device comprising:

[0032] A wavelet transform module, used for performing wavelet transform on image data, and decomposing the image data into multi-level wavelet coefficients;

[0033] A gradient quantization module, configured to perform gradient quantization processing on the multi-level wavelet coefficients in different regions;

[0034] The scanning module is used to perform main scanning and auxiliary scanning on the multi-level wavelet coefficients after regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm;

[0035] A coding judgment module, configured to dynamically judge whether the descendant nodes of a parent node need to be coded based on the parasitic flag bits of important coefficients during the main scanning process;

[0036] The target module is used to generate and transmit coded data based on the main scanning result, the auxiliary scanning result and the judgment result.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes an EZW-based image compression performance optimization device, which includes: a memory, a processor, and an EZW-based image compression performance optimization program stored on the memory and runnable on the processor, wherein the EZW-based image compression performance optimization program is configured to implement the steps of the EZW-based image compression performance optimization method described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, on which an EZW-based image compression performance optimization program is stored. When the EZW-based image compression performance optimization program is executed by a processor, the steps of the EZW-based image compression performance optimization method described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the EZW-based image compression performance optimization method as described above.

[0040] The present application decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions; performs main scan and auxiliary scan on the multi-level wavelet coefficients after regional gradient quantization in order from high frequency to low frequency based on the EZW algorithm; in the main scan process, dynamically judges whether the descendant nodes of the parent node need to be encoded based on the parasitic flag of the important coefficient; generates coded data based on the main scan result, the auxiliary scan result and the judgment result and transmits it. The present application decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions; performs main scan and auxiliary scan on the multi-level wavelet coefficients in order from high frequency to low frequency based on the EZW algorithm, and uses the zero tree structure to reduce the coding overhead of non-important coefficients. At the same time, by introducing the parasitic flag of the important coefficient in the main scan process, dynamically judges whether the descendant nodes of the parent node need to be encoded, further optimizing the coding efficiency and the utilization rate of the zero tree structure; finally, generates coded data based on the main scan, auxiliary scan and dynamic judgment results and transmits it, thereby improving the image compression performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flowchart of the first embodiment of the EZW-based image compression performance optimization method of this application;

[0042] Figure 2 This is a schematic diagram of a sub-flow in the second embodiment of the EZW-based image compression performance optimization method of this application;

[0043] Figure 3 This is a schematic diagram of a sub-flow in the third embodiment of the EZW-based image compression performance optimization method of this application;

[0044] Figure 4 This is a schematic diagram of the scanning process in an embodiment of the EZW-based image compression performance optimization method of this application;

[0045] Figure 5 This is a schematic diagram of the module structure of the EZW-based image compression performance optimization device according to an embodiment of the present application;

[0046] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the EZW-based image compression performance optimization method in the embodiment of the present application.

[0047] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] It should be noted that with the rapid development of digital integrated circuits, the demand for image processing technology in mobile devices continues to grow. To improve the efficiency of image transmission and storage, image compression technology has been widely researched and applied. In particular, in high-compression ratio scenarios, lossy compression algorithms are widely adopted, significantly reducing data volume at the expense of image quality. Wavelet-based image compression algorithms can further optimize compression performance. The embedded zerotree sweep (EZW) algorithm further enhances the performance of wavelet transform algorithms. The EZW algorithm exploits the correlation between parent and child wavelet transform coefficients to achieve efficient image compression based on a zerotree structure. However, in certain scenarios, when a parent node is determined to be an important coefficient, its descendant nodes are often redundantly encoded, which reduces compression performance. Furthermore, the distribution characteristics of wavelet coefficients across different frequency regions are not fully utilized, resulting in low quantization efficiency and, in turn, poor image compression performance. Therefore, improving image compression performance has become a pressing technical challenge.

[0051] The main solution of this application is: by performing wavelet transform on image data, the image data is decomposed into multi-level wavelet coefficients; the multi-level wavelet coefficients are subjected to regional gradient quantization processing; based on the EZW algorithm, the multi-level wavelet coefficients after regional gradient quantization processing are subjected to main scanning and auxiliary scanning in order from high frequency to low frequency; during the main scanning process, based on the parasitic flag of important coefficients, it is dynamically determined whether the descendant nodes of the parent node need to be encoded; based on the main scanning results, the auxiliary scanning results and the judgment results, the encoded data is generated and transmitted.

[0052] This application performs wavelet transform on image data to decompose it into multi-level wavelet coefficients, and combines regional gradient quantization processing to effectively reduce the amount of redundant data in different frequency regions; based on the EZW algorithm, main scanning and auxiliary scanning are performed in sequence from high frequency to low frequency, and the zero tree structure is used to reduce the coding overhead of non-important coefficients. At the same time, by introducing parasitic flags of important coefficients in the main scanning process, it is dynamically determined whether the descendant nodes of the parent node need to be encoded, further optimizing the coding efficiency and the utilization of the zero tree structure; finally, the main scanning, auxiliary scanning and dynamic judgment results are combined to generate encoded data and transmit it, thereby improving the image compression performance.

[0053] It should be noted that the execution entity of the method of this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the above-mentioned EZW-based image compression performance optimization device with the same or similar functions. This embodiment and the following embodiments will be described using the EZW-based image compression performance optimization device as an example.

[0054] Based on this, the first embodiment of the image compression performance optimization method based on EZW is proposed. Please refer to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the EZW-based image compression performance optimization method of this application.

[0055] In this embodiment, the EZW-based image compression performance optimization method includes the following steps:

[0056] S1: performing wavelet transform on the image data to decompose the image data into multi-level wavelet coefficients;

[0057] It should be noted that image data refers to a digitized two-dimensional image, usually represented in the form of a pixel matrix, where each pixel contains information about color or grayscale values. Wavelet transform is a mathematical transformation method that can decompose a signal into components of different scales and frequencies. Wavelet transform is particularly suitable for the analysis of non-stationary signals and can simultaneously extract the time and frequency information of the signal. Multi-level wavelet coefficients refer to the fact that the wavelet transform decomposes the image into multiple frequency levels, with each level corresponding to coefficients in a different frequency range. These coefficients reflect the characteristic information of the image at different scales, including low-frequency parts (global features) and high-frequency parts (detail information).

[0058] Specifically, appropriate wavelet basis functions (such as Haar wavelets and Daubechies wavelets) and the number of decomposition levels are selected based on the characteristics of the image data. The wavelet basis functions determine the basic shape of the wavelet transform and influence the expressiveness of the result. The number of decomposition levels controls the depth of the image decomposition. The higher the number of levels, the lower the resolution of the low-frequency components and the less detail in the high-frequency components. The image data is then preprocessed and represented as a two-dimensional pixel matrix, providing input for the subsequent wavelet transform.

[0059] Furthermore, a one-dimensional wavelet transform is applied to each row of the image matrix to decompose it into a low-frequency component (L) and a high-frequency component (H), which represent the main contour information and local detail information of the image, respectively. Half of the output matrix is ​​the low-frequency part and half is the high-frequency part. A one-dimensional wavelet transform is applied again to each column of the matrix resulting from the row transform to obtain four sub-bands: low-frequency low-frequency (LL), low-frequency high-frequency (LH), high-frequency low-frequency (HL) and high-frequency high-frequency (HH). These sub-bands represent the characteristics of the image in different directions and frequency ranges, respectively. For multi-level wavelet transform, the low-frequency sub-band (LL) is repeatedly decomposed to form deeper multi-level wavelet coefficients.

[0060] The wavelet transform decomposes image data into multi-level wavelet coefficients, whereby the low-frequency components focus on representing global image information, while the high-frequency components primarily reflect detailed image features. This decomposition provides the basis for subsequent targeted compression operations, allowing the characteristics of different regions to be processed independently. Unlike the traditional Fourier transform, the wavelet transform preserves information in both the spatial and frequency domains, making it more effective in representing image edges and details, making it particularly well-suited for image compression and reconstruction. After multi-level wavelet decomposition, the majority of the image energy is concentrated in the low-frequency components, while the high-frequency components contain less information and are more redundant. By further quantizing and optimizing the high-frequency components, the amount of data stored and transmitted can be significantly reduced without significantly affecting overall image quality. The multi-level wavelet coefficients provide a hierarchical image representation, forming the basis for subsequent operations such as region-by-region quantization and EZW scanning, enabling the algorithm to achieve a flexible balance between compression ratio and decoding quality.

[0061] S2: performing regional gradient quantization processing on the multi-level wavelet coefficients;

[0062] It should be noted that region division refers to dividing the wavelet coefficients into different regions based on their frequency characteristics, such as low-frequency regions and high-frequency regions. Different regions correspond to different image features. Gradient quantization assigns different quantization factors based on the importance of each region. By more finely quantizing important regions (such as low-frequency regions) and coarsely quantizing less important regions (such as high-frequency regions), a balance is achieved between compression and information preservation.

[0063] Specifically, the multi-level wavelet coefficients are divided into low-frequency and high-frequency regions based on their frequency distribution characteristics. The low-frequency region (LL subband) contains the main energy and global features of the image and is generally the most important region. The high-frequency region (LH, HL, and HH subbands) contains local details and texture information of the image and is relatively less important. In the multi-level wavelet transform result, the importance of high-frequency regions at higher decomposition levels gradually decreases. High-frequency regions at lower decomposition levels generally contain more important details.

[0064] Furthermore, different quantization factors are set based on the importance of each frequency region. Low-frequency regions use smaller quantization factors to preserve global features and key information as much as possible. High-frequency regions use larger quantization factors to reduce the precision requirements for less important details, thereby reducing the amount of data. Each frequency region is traversed and the wavelet coefficients within that region are quantized. The coefficients are scaled using the quantization factor to retain valid information with a certain accuracy while truncating redundant data below the quantization accuracy.

[0065] Regional gradient quantization effectively reduces redundant data and the overall amount of data stored and transmitted by introducing larger quantization factors in high-frequency areas, thereby achieving efficient compression. Differentiated quantization strategies are adopted for different regions, allowing the compression algorithm to maximize the retention of important information while reducing the amount of data, thereby improving the compression ratio. Using smaller quantization factors in low-frequency areas ensures that the main feature information of the image is retained, improving the restoration quality of the image after decoding. The quantization factor allocation strategy is dynamically adjusted according to the frequency characteristics of the image content, making the method more adaptable to different types of images. Through regional gradient quantization processing, the storage efficiency and information utilization of multi-level wavelet coefficients are significantly improved, providing a more optimized input for subsequent EZW scanning and encoding.

[0066] S3: Based on the EZW algorithm, the multi-level wavelet coefficients after the regional gradient quantization processing are subjected to main scanning and auxiliary scanning in the order from high frequency to low frequency.

[0067] It should be noted that the EZW algorithm, short for Embedded Zerotree Wavelet, is a highly efficient image compression algorithm that leverages the parent-child node correlation of wavelet transform coefficients to achieve efficient coefficient encoding through a zerotree structure. The main scan, the first stage of the EZW algorithm, is primarily used to identify important coefficients and construct a zerotree structure, determining which coefficients require further processing based on the current threshold. The auxiliary scan, the second stage of the EZW algorithm, performs refined encoding on the portions of the main scan marked as important coefficients, including sign encoding and absolute value interval refinement.

[0068] Specifically, the initial scanning conditions are determined, including the value of the maximum coefficient as the starting reference value. This helps accurately determine which coefficients are important in the first round of scanning. The scanning order is determined, starting with the high-frequency regions at the highest decomposition level and gradually transitioning to low-frequency regions. This order helps prioritize detailed information while gradually preserving the global characteristics of the image.

[0069] Furthermore, the main scan is responsible for traversing each wavelet coefficient and classifying them according to pre-set criteria. If a coefficient is highly important, it is marked as an "important coefficient," indicating that it requires further processing and encoding. If a coefficient and its child nodes are unimportant, it is marked as a "zerotree root" to avoid repeated processing of its descendant nodes. If a coefficient itself is unimportant but its child nodes contain important information, it is marked as an "isolated zero" to focus on these child nodes in subsequent processing. The results of the main scan are recorded, including the location and classification of each important coefficient. This information is organized into a "significant coefficient secondary table" to provide the basis for the auxiliary scan. The auxiliary scan is responsible for further processing the important coefficients marked by the main scan. The specific values ​​of each important coefficient are further divided and their performance within different intervals is recorded. The positive and negative information of these important coefficients is also recorded to ensure the integrity of the image during reconstruction. The auxiliary scan results are ultimately combined with the zerotree structure information from the main scan to form the complete encoded data.

[0070] The main scan leverages the zerotree structure to directly mark a large number of unimportant coefficients and their descendant coefficients as "zerotree roots," avoiding repeated scanning and processing of descendant nodes and significantly reducing the amount of data processing. The auxiliary scan performs refined encoding on the important coefficients marked in the main scan, ensuring that this important information is accurately retained. This refined processing method effectively balances compression ratio and image quality. The main scan quickly filters unnecessary data, while the auxiliary scan finely encodes key data. Combined with the rational design of the scanning sequence, the entire compression process is both efficient and takes into account image reconstruction quality. The scanning sequence from high frequency to low frequency prioritizes processing detailed information, making the zerotree structure more efficient in high-frequency regions while fully preserving the global features of low-frequency regions. Through the coordination of the main scan and auxiliary scan, the EZW algorithm can efficiently process the wavelet coefficients after gradient quantization in each region, achieving an optimal balance between high compression ratio and high restoration accuracy.

[0071] S4: During the main scanning process, dynamically determine whether the descendant nodes of the parent node need to be encoded based on the parasitic flag of the important coefficient;

[0072] It's important to note that the Parasitic Important Coefficient Flag (PAF) dynamically marks whether a parent node's descendant nodes require encoding. When a parent node has an important coefficient, the parasitic flag indicates whether its descendant nodes have important coefficients, thereby determining whether to skip further scanning of the descendant nodes. Dynamic determination involves generating a real-time determination based on the coefficient status (important or unimportant) during the actual scanning process, and adjusting the subsequent scanning logic.

[0073] Specifically, at the beginning of the main scan, an initial threshold is set based on the maximum value of the multi-level wavelet coefficients as the criterion for determining whether a coefficient is important. A parasitic flag for important coefficients is dynamically set for each parent node. Initially, it is empty or has a default value. This flag is used to determine whether to scan and encode descendant nodes.

[0074] Furthermore, if the absolute value of the coefficient of the current node is greater than or equal to the current threshold, the node is marked as an important coefficient. If the current node is marked as an important coefficient, its descendant nodes are further judged. If all descendant nodes are unimportant coefficients: the parent node's parasitic flag is set to "1", indicating that the descendant nodes can be skipped for encoding. If there are important coefficients in the descendant nodes: the parent node's parasitic flag is set to "0", indicating that the descendant nodes need to continue encoding. The scanning process is dynamically adjusted according to the value of the parasitic flag: PAF = 1: Skip the scanning and encoding of the descendant nodes to reduce redundant processing; PAF = 0: Continue scanning and judging the descendant nodes to ensure that important information is correctly encoded. After the main scan is completed, the status of each important coefficient (such as position, value, sign, etc.) and the marking result of the parasitic flag are recorded in the important coefficient sub-table, which serves as input for the subsequent auxiliary scan. At the same time, the zerotree structure information is updated. Combined with the optimized zerotree of the parasitic flag, it can more efficiently guide the encoding process.

[0075] The parasitic flag for significant coefficients dynamically determines whether a parent node's descendant nodes require encoding. For descendant nodes lacking important information, scanning and encoding are skipped, avoiding unnecessary processing and improving the algorithm's overall efficiency. The parasitic flag enhances the expressive power of the zerotree structure, enabling it to more accurately represent the correlations between wavelet coefficients, further improving the compression ratio. The dynamic parasitic flag determination mechanism adjusts scanning and encoding strategies based on actual data distribution, making the algorithm more flexible and suitable for both low-compression ratio scenarios and high-compression ratio scenarios, while maintaining high image quality. By reducing the need to encode descendant nodes, the parasitic flag effectively reduces the amount of encoded data, thereby shortening encoding time and reducing storage requirements.

[0076] S5: Based on the main scanning result, the auxiliary scanning result and the judgment result, the coded data is generated and transmitted.

[0077] It should be noted that the judgment result refers to the result of dynamically determining the encoding requirements of parent and descendant nodes based on the parasitic flags of important coefficients during the main scan process. This is used to optimize the zerotree structure and compression process. The encoded data refers to the compressed data file formed by combining the main scan, auxiliary scan, and judgment results for transmission or storage, containing the complete information required for image recovery.

[0078] Specifically, the important coefficients, secondary table information, and zerotree structure marker data generated by the main scan are extracted. Combined with dynamic judgment results (such as parasitic flags for important coefficients), the optimized zerotree structure can directly mark which descendant nodes are skipped for encoding. The fine-coded data generated by the secondary scan phase is matched one-to-one with the important coefficients marked in the main scan, including the binary encoding of the sign information and absolute value. This ensures that the secondary scan results cover all important coefficients marked in the main scan.

[0079] Furthermore, the zerotree structure information is converted into a compact coding form to record the status of each node (such as important coefficients, zerotree roots, isolated zeros) and the parent-child node relationship. For descendant nodes marked as not requiring coding in the dynamic judgment results, the relevant data is directly omitted to further compress the coding amount. The refined coding results generated by the auxiliary scan are combined with the zerotree structure to form complete coefficient coding data, including: the precise value coding of important coefficients, sign information, and related zerotree tags. The main scan, auxiliary scan and dynamic judgment results are integrated into a compressed data file to ensure data compactness and decoding integrity, including all information required for image reconstruction, such as important coefficients, sub-tables, zerotree tags and optimized parasitic flag information.

[0080] By integrating the results of the main and auxiliary scans and combining them with a zerotree structure optimized for parasitic flags, the amount of redundant data encoded can be minimized, further improving compression efficiency. The zerotree markings of the main scan provide global compression information, while the refined encoding of the auxiliary scan ensures high-precision representation of important coefficients. This combination ensures high-quality restoration of the decoded image under high compression ratios. The resulting encoded data is small and compact, making it suitable for efficient transmission in bandwidth-constrained scenarios or long-term storage in environments with limited storage resources. Dynamically optimizing the zerotree structure adapts to the coefficient distribution characteristics of different images, ensuring that the generated encoded data maintains a balance between high compression ratio and high decoding quality for all image types.

[0081] This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed on the multi-level wavelet coefficients after regional gradient quantization in order from high frequency to low frequency. During the main scan, based on the parasitic flag of important coefficients, whether the descendant nodes of the parent node need to be encoded is dynamically determined. Based on the main scan result, auxiliary scan result and judgment result, coded data is generated and transmitted. This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed in order from high frequency to low frequency, and a zerotree structure is used to reduce the coding overhead of non-important coefficients. At the same time, by introducing the parasitic flag of important coefficients in the main scan process, whether the descendant nodes of the parent node need to be encoded is dynamically determined, further optimizing the coding efficiency and the utilization of the zerotree structure. Finally, coded data is generated and transmitted based on the main scan, auxiliary scan and dynamic judgment result, thereby improving image compression performance.

[0082] Based on the above first embodiment, the second embodiment of the image compression performance optimization method based on EZW is proposed. Figure 2 , Figure 2 This is a schematic diagram of a sub-flow in the second embodiment of the EZW-based image compression performance optimization method of this application.

[0083] like Figure 2 As shown, in this embodiment, step S1 includes:

[0084] S11: extracting the frequency distribution characteristics of the image data, and determining the target wavelet basis function and the number of decomposition layers according to the frequency distribution characteristics;

[0085] S12: Based on the target wavelet basis function and the number of decomposition layers, decomposing the image data in a row direction and a column direction to obtain a plurality of sub-bands;

[0086] S13: performing the row-wise decomposition and the column-wise decomposition on the row and column low-frequency sub-bands in the plurality of sub-bands to obtain multi-level sub-bands;

[0087] S14: Determine the multi-level wavelet coefficients according to the multiple sub-bands and the multi-level sub-bands.

[0088] It should be noted that frequency distribution characteristics refer to the distribution patterns of different frequency components in image data (such as low-frequency global information and high-frequency detail information), which reflect the complexity and structural characteristics of the image content.

[0089] Specifically, statistical analysis is performed on the image data to extract its frequency distribution characteristics, such as texture complexity, edge density, and global contrast. The frequency distribution characteristics are used to determine whether the main information of the image is concentrated in the low-frequency part or the high-frequency part. The appropriate target wavelet basis function is selected according to the frequency distribution characteristics. For example: for images with obvious edges, the Haar wavelet can be selected to better capture the edge characteristics. For images containing smooth areas and detail information, the Daubechies wavelet can be selected to more accurately represent information at different levels. The number of wavelet decomposition layers is determined based on the size of the image and the frequency distribution range. The higher the number of decomposition layers, the lower the resolution of the low-frequency part, which is suitable for images with large size and complex textures; the lower the number of decomposition layers, the more suitable it is for simple images.

[0090] Furthermore, each row of the image matrix is ​​subjected to high-pass and low-pass filtering to extract high-frequency and low-frequency information, respectively, and generate preliminary sub-band components. Each column of the result matrix after row decomposition is then subjected to high-pass and low-pass filtering to further separate the frequency components in different directions, obtaining four sub-bands: LL, LH, HL, and HH. The low-frequency sub-band (LL) is further decomposed in the row and column directions, and the above process is repeated to extract lower frequency information layer by layer. After each decomposition, the resolution of the low-frequency sub-band decreases, while the high-frequency sub-band contains less detailed information layer by layer. Through multiple decompositions, the image data is decomposed into multiple frequency sub-bands at different levels, each sub-band contains its specific frequency components, and ultimately generates multi-level wavelet coefficients.

[0091] By selecting the target wavelet basis function and the number of decomposition levels based on the frequency distribution characteristics of the image, the wavelet transform can more accurately represent the main information and detailed features of the image, thereby improving the efficiency and accuracy of subsequent processing. Multi-level wavelet decomposition can distribute image information across subbands of different frequencies and directions, clearly distinguishing global features (low frequencies) from local details (high frequencies), and providing a good data structure for subsequent compression and analysis. Further decomposition of low-frequency subbands retains the main global information, while the step-by-step refinement of high-frequency subbands allows for the reasonable preservation of details, providing an efficient foundation for subsequent compression. By dynamically selecting the wavelet basis function and the number of decomposition levels, the method can adapt to images of different types and complexities, effectively balancing compression ratio and decoding quality.

[0092] Based on the above first embodiment, in this embodiment, step S2 includes:

[0093] S21: Dividing the multi-level wavelet coefficients into multiple frequency regions based on the sub-band frequencies and the number of decomposition levels of the multi-level wavelet coefficients;

[0094] S22: determining a quantization factor allocation strategy according to characteristics of the image data in each frequency region;

[0095] S23: Based on the quantization factor allocation strategy, quantize the sub-band coefficients corresponding to the respective frequency regions.

[0096] It should be noted that subband frequency refers to the frequency components contained in different subbands generated after wavelet transform, such as low-frequency subband (LL, containing global information) and high-frequency subband (LH, HL, HH, containing detail information). The frequency region divides the wavelet coefficients into several specific frequency regions based on the frequency range and decomposition level of the subband, corresponding to the global features and detail information of the image respectively. The quantization factor refers to the scaling parameter applied to the wavelet coefficients during the quantization process, which is used to balance image quality and compression efficiency. The quantization factor allocation strategy refers to allocating appropriate quantization factors to different frequency regions based on their importance and characteristics to achieve optimized quantization processing.

[0097] Specifically, the frequency characteristics of different subbands in the multi-level wavelet coefficients are analyzed and divided into several frequency regions: the low-frequency region, composed of the LL subband, primarily contains the global energy and key feature information of the image; the high-frequency region, composed of the LH, HL, and HH subbands, reflects the image's details and texture information. For the multi-level decomposition results, the low-frequency and high-frequency subbands at each level are further refined: the low-frequency regions at higher decomposition levels retain the global structure; the high-frequency regions at higher decomposition levels represent local details, with decreasing importance at each level.

[0098] Furthermore, low-frequency regions have concentrated energy, significantly impacting image reconstruction quality and requiring a smaller quantization factor to ensure accuracy. High-frequency regions contain detailed information such as texture and edges, which have less impact on visual perception and therefore allow for a larger quantization factor to compress data. The quantization factor is dynamically adjusted based on the importance of each frequency region and its impact on image quality: low-frequency regions use a small quantization factor to preserve key information, while high-frequency regions use a large quantization factor to reduce redundant data. The higher the decomposition level, the less important the high-frequency region, and the larger the quantization factor.

[0099] Optionally, for each frequency region, the wavelet coefficients are read one by one. The wavelet coefficients in each subband are scaled using a predetermined quantization factor: larger coefficients are retained to preserve important information, while smaller coefficients are approximated to zero to reduce the amount of data. The quantized wavelet coefficients are truncated or rounded to ensure that they meet compression requirements while avoiding excessive loss of data accuracy.

[0100] Different quantization strategies are employed for low-frequency and high-frequency regions, effectively reducing the storage of unimportant information and achieving higher compression ratios. Smaller quantization factors are used in low-frequency regions to preserve global information, ensuring that the key features of the compressed image are restored, thereby guaranteeing decoded image quality. Larger quantization factors are used to significantly reduce the coefficients in high-frequency regions, reducing data redundancy and lowering storage and transmission overhead. The quantization factor allocation strategy is dynamically adjusted based on image characteristics, enabling the algorithm to adapt to images of varying types and complexities, achieving an optimal balance between compression efficiency and image restoration quality.

[0101] This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed on the multi-level wavelet coefficients after regional gradient quantization in order from high frequency to low frequency. During the main scan, based on the parasitic flag of important coefficients, whether the descendant nodes of the parent node need to be encoded is dynamically determined. Based on the main scan result, auxiliary scan result and judgment result, coded data is generated and transmitted. This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed in order from high frequency to low frequency, and a zerotree structure is used to reduce the coding overhead of non-important coefficients. At the same time, by introducing the parasitic flag of important coefficients in the main scan process, whether the descendant nodes of the parent node need to be encoded is dynamically determined, further optimizing the coding efficiency and the utilization of the zerotree structure. Finally, coded data is generated and transmitted based on the main scan, auxiliary scan and dynamic judgment result, thereby improving image compression performance.

[0102] Based on the above second embodiment, the third embodiment of the image compression performance optimization method based on EZW is proposed. Figure 3 , Figure 3 This is a schematic diagram of a sub-flow in the third embodiment of the EZW-based image compression performance optimization method of this application.

[0103] In this embodiment, step S3 includes:

[0104] S31: determining a scanning threshold according to the maximum absolute value of the multi-level wavelet coefficients after the regional gradient quantization processing;

[0105] S32: performing the main scan on the multi-level wavelet coefficients after the gradient quantization processing of the sub-regions in order from high frequency to low frequency, and comparing the main scan result with the scan threshold;

[0106] S33: generating an important coefficient sub-table based on the comparison result, wherein the important coefficient sub-table includes important coefficients and positions of the important coefficients;

[0107] S34: Based on the important coefficient sub-table, perform the auxiliary scanning on the important coefficients.

[0108] It should be noted that the scanning threshold is used to determine whether a wavelet coefficient is an important coefficient. It is dynamically set according to the maximum absolute value of the wavelet coefficient and gradually decreases with each scanning round. The important coefficient sub-table records the data determined to be important coefficients in the main scan, including the value, sign and position of these coefficients in the wavelet coefficient matrix, providing input for the auxiliary scan.

[0109] Specifically, the maximum absolute value is extracted from the multi-level wavelet coefficients after regional gradient quantization processing as a reference for the initial scanning threshold. The scanning threshold is set based on the maximum absolute value for the first round of main scanning. In subsequent scans, the scanning threshold is gradually reduced (for example, halved each round) to gradually refine the judgment and encoding. Starting from the high-frequency area of ​​the highest decomposition layer, gradually scan to the low-frequency area of ​​the lowest decomposition layer, giving priority to high-frequency coefficients containing detail information. Traverse each wavelet coefficient to determine whether its absolute value is greater than or equal to the current scanning threshold. If the absolute value is greater than or equal to the threshold, it is marked as an important coefficient. If the absolute value is less than the threshold, and the coefficient of the descendant node is also less than the threshold, it is marked as a zero tree root. If the absolute value is less than the threshold, but there are important coefficients in the descendant node, it is marked as an isolated zero.

[0110] Furthermore, based on the main scan results, an important coefficient sub-table is generated, which contains the numerical value, sign and position information of the important coefficients. The zero tree structure information is also recorded in the sub-table for the complete representation of subsequent auxiliary scans and compressed data. Each coefficient in the important coefficient sub-table is traversed and its numerical value is further encoded. The absolute value of the important coefficient is divided into multiple intervals, and the corresponding refined code is generated according to the interval to which it belongs. At the same time, the sign (positive or negative) of the important coefficient is recorded to ensure that the correct value can be restored when the image is decoded. The coded data generated by the auxiliary scan is combined with the zero tree structure recorded by the main scan to form complete compressed coded data.

[0111] By setting the scan threshold based on the maximum absolute value and gradually lowering the threshold over multiple scans, it is possible to effectively distinguish between important and unimportant coefficients, making the encoding process more efficient. The main scan quickly marks important coefficients and the zerotree structure, significantly reducing redundant processing of descendant nodes; the auxiliary scan further refines the encoding of important coefficients to ensure accuracy and compactness of the encoding. The construction of the zerotree structure and the important coefficient sub-table effectively records the parent-child node relationship of the wavelet coefficients, skipping unimportant coefficients and avoiding unnecessary data processing. The efficient zerotree marking in the main scan combined with the refined encoding of the auxiliary scan enables the algorithm to maintain a high compression ratio while ensuring image reconstruction quality. Through the dynamic adjustment of the scan threshold and the combination of main and auxiliary scans, this step achieves efficient encoding of image data, providing core support for improving image compression performance.

[0112] Based on the above second embodiment, in this embodiment, step S34 includes:

[0113] S341: traverse the important coefficient sub-table and perform refinement coding on the absolute values ​​of the important coefficients;

[0114] S342: Determine whether the absolute value of the important coefficient is within a preset range;

[0115] S343: If yes, generate a corresponding binary code sequence based on the refined coding result, and record the positive and negative information of the important coefficients.

[0116] It should be noted that refined coding refers to the process of more precisely encoding the absolute values ​​of significant coefficients, further dividing their numerical ranges to reduce data redundancy. Preset intervals divide the absolute values ​​of significant coefficients into fixed ranges, which are used to determine the interval within which they fall, thereby determining the encoding method. A binary code sequence is the compressed data generated by encoding the absolute values ​​and sign information of significant coefficients, represented in binary form for ease of storage and transmission.

[0117] Specifically, the absolute value of each important coefficient and its corresponding position information and symbol are extracted from the important coefficient sub-table in sequence. The coefficients in the sub-table are arranged in the order of the main scan to ensure the orderliness of the encoding process. The absolute values ​​of the important coefficients are divided into multiple intervals, each of which corresponds to a specific encoding rule. For example, a smaller absolute value interval corresponds to a shorter code; a larger absolute value interval may require more bits to represent. For the absolute value of each important coefficient, the preset interval range it falls into is determined. The corresponding refined code is generated according to the interval to which the coefficient belongs, such as a fixed-length or adaptive-length binary code.

[0118] Furthermore, based on the absolute value encoding, sign information is added: if the coefficient is positive, it is recorded as a "positive flag"; if the coefficient is negative, it is recorded as a "negative flag." The refined absolute value encoding and sign marking are combined to generate a complete binary code sequence, representing the compressed data of the important coefficient.

[0119] By refining the encoding of the absolute values ​​of important coefficients, the numerical range is further accurately represented, which significantly reduces data redundancy and improves compression efficiency. The division of preset intervals can dynamically adapt to the distribution characteristics of important coefficients. For large-range values, more bits of encoding are used, while for small-range values, shorter encoding is used, which improves compression adaptability. Sign information is appended while recording the binary code to ensure that the positive and negative attributes of each important coefficient can be correctly restored during decoding, thereby improving the quality of image reconstruction. The generated binary code sequence has a compact structure, which is suitable for efficient transmission in bandwidth-limited scenarios and is convenient for high-density storage in storage devices. Through refined coding, interval judgment and symbol recording, this step achieves accurate encoding and efficient representation of important coefficients, providing optimized support for high-quality storage and transmission of compressed data.

[0120] Based on the above second embodiment, in this embodiment, after step S3, the following steps are further included:

[0121] S3a: reducing the value of the scanning threshold based on a preset threshold reduction strategy;

[0122] S3b: Repeat the main scan and the auxiliary scan until the scan threshold decreases to a preset minimum threshold.

[0123] It should be noted that the preset threshold reduction strategy refers to dynamically adjusting the size of the scanning threshold according to specific rules, so that it is gradually reduced to refine the processing of wavelet coefficients layer by layer. Common strategies include fixed-proportional reduction (such as halving each time) or adaptive adjustment. The scanning threshold is a benchmark value used to determine whether a wavelet coefficient is an important coefficient. It gradually decreases as the scanning round proceeds to capture more important coefficients with low amplitude. The preset minimum threshold is the lower limit of the pre-set scanning threshold, usually set to 1 or other smaller values, and is used to determine the scanning termination condition.

[0124] Specifically, the initial scan threshold is determined based on the maximum absolute value of the multi-level wavelet coefficients. This threshold is used to identify the most significant coefficients in the first main scan. The threshold is adjusted according to a preset threshold reduction strategy: Fixed-ratio reduction: For example, the current threshold is halved each time, making the scanning process gradually more refined. Adaptive adjustment: The threshold is dynamically adjusted based on the number of significant coefficients marked in the previous scan. If there are many significant coefficients, the threshold amplitude is appropriately reduced, otherwise it is conservatively reduced. The new threshold is recorded after each scan to ensure that the next scan can capture coefficients with smaller amplitudes but still significant.

[0125] Furthermore, the current threshold is used to scan the multi-level wavelet coefficients after regional gradient quantization processing to determine which coefficients are important coefficients, generate a zero tree structure and record them in the important coefficient sub-table. Skip the coefficients below the threshold and their descendant nodes, optimize data processing through the zero tree structure, and reduce redundancy. Further refine the encoding of the important coefficients marked by the main scan to generate the corresponding absolute value encoding and sign information. Combine the auxiliary scan results with the zero tree structure of the main scan to gradually improve the encoded data. After each time the scan threshold is lowered, repeat the main scan and auxiliary scan to capture more important information and gradually refine the encoding process. When the scan threshold is reduced to the preset minimum threshold, stop further main scan and auxiliary scan operations. At this point, all possible important coefficients have been recorded and finely encoded, and the compressed data has been completed. Integrate the results of all rounds of main scans and auxiliary scans, including zero tree structure information and important coefficient encoding, to generate a complete compressed file.

[0126] Dynamically lowering the scanning threshold allows the main and auxiliary scans to capture smaller, but still significant, coefficients layer by layer, gradually refining the encoding results and ensuring accurate image compression. Focusing on significant coefficients in the initial scan, and then gradually lowering the threshold over multiple rounds, effectively avoids premature processing of unimportant data, improving overall encoding efficiency. Successively lowering the threshold captures more important, low-amplitude coefficients, ensuring that the compressed data contains sufficient information, thereby maintaining image reconstruction quality even at high compression ratios. Dynamically adjusting the processing depth through a preset threshold reduction strategy allows the algorithm to flexibly adapt to the coefficient distribution characteristics of different images, achieving a balance between compression effectiveness and decoding quality. The introduction of a zerotree structure combined with layer-by-layer threshold adjustment avoids repeated processing of descendant nodes, significantly reducing the amount of redundant data during the encoding process.

[0127] This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed on the multi-level wavelet coefficients after regional gradient quantization in order from high frequency to low frequency. During the main scan, based on the parasitic flag of important coefficients, whether the descendant nodes of the parent node need to be encoded is dynamically determined. Based on the main scan result, auxiliary scan result and judgment result, coded data is generated and transmitted. This embodiment decomposes the image data into multi-level wavelet coefficients by performing wavelet transform on the image data, and combines regional gradient quantization to effectively reduce the amount of redundant data in different frequency regions. Based on the EZW algorithm, main scan and auxiliary scan are performed in order from high frequency to low frequency, and a zerotree structure is used to reduce the coding overhead of non-important coefficients. At the same time, by introducing the parasitic flag of important coefficients in the main scan process, whether the descendant nodes of the parent node need to be encoded is dynamically determined, further optimizing the coding efficiency and the utilization of the zerotree structure. Finally, coded data is generated and transmitted based on the main scan, auxiliary scan and dynamic judgment result, thereby improving image compression performance.

[0128] For example, see Figure 4 , Figure 4 This is a schematic diagram of the scanning process in an embodiment of the EZW-based image compression performance optimization method of this application.

[0129] like Figure 4 As shown in the figure, for nodes with descendant nodes, an important coefficient parasitic flag (PAF) is added. When the node is scanned as a non-important coefficient, the parasitic flag (PAF) does not exist and does not affect the normal scanning performance of EZW. When the node is scanned as a main coefficient, the parasitic flag (PAF) exists, which means:

[0130] 0: The descendant nodes of this node have important coefficients, and the descendant nodes cannot be skipped for coding.

[0131] 1: There are no important coefficients in the descendant nodes of this node, and the encoding of the descendant nodes can be skipped.

[0132] When using the parasitic important coefficient flag (PAF), in order to better improve the effect, the coefficients of the wavelet transform need to be gradient quantized in different regions. The lower the frequency of the coefficient region, the smaller the quantization coefficient used. In this way, it is ensured that the child node has a higher probability of not being scanned together with the parent node, thereby increasing the transmission efficiency.

[0133] The present application also provides an EZW-based image compression performance optimization device, please refer to Figure 5 , Figure 5This is a schematic diagram of the module structure of the EZW-based image compression performance optimization device according to an embodiment of the present application. The EZW-based image compression performance optimization device includes:

[0134] The wavelet transform module 501 is used to perform wavelet transform on the image data and decompose the image data into multi-level wavelet coefficients;

[0135] A gradient quantization module 502 is configured to perform gradient quantization processing on the multi-level wavelet coefficients in different regions;

[0136] The scanning module 503 is used to perform main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm;

[0137] The coding judgment module 504 is used to dynamically judge whether the descendant nodes of the parent node need to be coded based on the parasitic flag of the important coefficients during the main scanning process;

[0138] The target module 505 is used to generate and transmit coded data based on the main scanning result, the auxiliary scanning result and the judgment result.

[0139] The EZW-based image compression performance optimization device provided in the embodiments of this application utilizes the EZW-based image compression performance optimization method of the aforementioned embodiments to solve the technical problem of how to improve image compression performance. Compared to the prior art, the beneficial effects of the EZW-based image compression performance optimization device provided in the embodiments of this application are the same as those of the EZW-based image compression performance optimization method provided in the aforementioned embodiments. Other technical features of the EZW-based image compression performance optimization device are the same as those disclosed in the aforementioned embodiments and are not further described here.

[0140] The present application provides an EZW-based image compression performance optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the EZW-based image compression performance optimization method in the above-mentioned embodiment.

[0141] Reference below Figure 6 , Figure 6The following is a schematic diagram of the hardware operating environment involved in the EZW-based image compression performance optimization method according to an embodiment of the present application, illustrating a schematic diagram of the structure of an EZW-based image compression performance optimization device suitable for implementing the embodiment of the present application. The EZW-based image compression performance optimization device according to the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The EZW-based image compression performance optimization device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0142] like Figure 6 As shown, the EZW-based image compression performance optimization device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the EZW-based image compression performance optimization device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow the EZW-based image compression performance optimization device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an EZW-based image compression performance optimization device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0143] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0144] The EZW-based image compression performance optimization device provided in this application, employing the EZW-based image compression performance optimization method described in the aforementioned embodiment, can address the technical problem of improving image compression performance. Compared to the prior art, the beneficial effects of the EZW-based image compression performance optimization device provided in this application are the same as those of the EZW-based image compression performance optimization method described in the aforementioned embodiment. Other technical features of the EZW-based image compression performance optimization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0145] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0146] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the EZW-based image compression performance optimization method in the above embodiment.

[0148] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0149] The computer-readable storage medium may be included in the EZW-based image compression performance optimization device, or may exist independently without being incorporated into the EZW-based image compression performance optimization device.

[0150] The computer-readable storage medium carries one or more programs. When executed by an EZW-based image compression performance optimization device, the one or more programs cause the EZW-based image compression performance optimization device to: perform a wavelet transform on image data to decompose the image data into multi-level wavelet coefficients; perform region-by-region gradient quantization on the multi-level wavelet coefficients; perform a main scan and a secondary scan on the multi-level wavelet coefficients after the region-by-region gradient quantization in order from high frequency to low frequency based on the EZW algorithm; during the main scan, dynamically determine whether the descendant nodes of the parent node need to be encoded based on the parasitic flag of the important coefficients; and generate and transmit encoded data based on the main scan result, the secondary scan result, and the judgment result. The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0151] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0152] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0153] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned EZW-based image compression performance optimization method, thereby solving the technical problem of improving image compression performance. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the EZW-based image compression performance optimization method provided in the aforementioned embodiment, and are not further elaborated here.

[0154] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned EZW-based image compression performance optimization method.

[0155] The computer program product provided in this application can solve the technical problem of how to improve image compression performance. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the EZW-based image compression performance optimization method provided in the above embodiments, and will not be repeated here.

[0156] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. An image compression performance optimization method based on EZW, characterized in that: The method comprises: Performing wavelet transform on the image data to decompose the image data into multi-level wavelet coefficients; Performing regional gradient quantization processing on the multi-level wavelet coefficients; Based on the EZW algorithm, the multi-level wavelet coefficients after the regional gradient quantization are subjected to main scanning and auxiliary scanning in the order from high frequency to low frequency. In the main scanning process, based on the parasitic flag of the important coefficient, dynamically determine whether the descendant nodes of the parent node need to be encoded; Based on the main scanning result, the auxiliary scanning result and the judgment result, the encoded data is generated and transmitted.

2. The method according to claim 1, characterized in that The step of performing wavelet transform on the image data to decompose the image data into multi-level wavelet coefficients comprises: Extracting frequency distribution characteristics of the image data, and determining a target wavelet basis function and a number of decomposition layers according to the frequency distribution characteristics; Based on the target wavelet basis function and the number of decomposition layers, the image data is decomposed in a row direction and a column direction to obtain a plurality of sub-bands; Performing the row-wise decomposition and the column-wise decomposition on the row and column low-frequency sub-bands in the multiple sub-bands to obtain multi-level sub-bands; The multi-level wavelet coefficients are determined according to the multiple sub-bands and the multi-level sub-bands.

3. The method according to claim 1, characterized in that The step of performing regional gradient quantization processing on the multi-level wavelet coefficients comprises: Based on the subband frequency and the number of decomposition levels of the multi-level wavelet coefficients, the multi-level wavelet coefficients are divided into a plurality of frequency regions; Determining a quantization factor allocation strategy according to characteristics of the image data in each frequency region; Based on the quantization factor allocation strategy, quantization processing is performed on the subband coefficients corresponding to each frequency region.

4. The method according to claim 1, characterized in that The step of performing main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm includes: Determining a scanning threshold according to the maximum absolute value of the multi-level wavelet coefficients after the sub-region gradient quantization processing; Performing the main scanning on the multi-level wavelet coefficients after the sub-region gradient quantization processing in the order from high frequency to low frequency, and comparing the main scanning result with the scanning threshold; Based on the comparison result, generate an important coefficient sub-table, wherein the important coefficient sub-table includes important coefficients and positions of the important coefficients; Based on the important coefficient sub-table, the auxiliary scanning is performed on the important coefficients.

5. The method according to claim 4, characterized in that The step of performing the auxiliary scanning on the important coefficients based on the important coefficient sub-table comprises: Traversing the important coefficient sub-table, and performing refinement encoding on the absolute values ​​of the important coefficients; Determining whether the absolute value of the important coefficient is within a preset interval; If so, based on the refined coding result, a corresponding binary coding sequence is generated, and the positive and negative information of the important coefficients are recorded.

6. The method according to claim 4, characterized in that After the step of performing main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm, the method further includes: Based on a preset threshold reduction strategy, reducing the value of the scanning threshold; The main scan and the auxiliary scan are repeatedly performed until the scan threshold is reduced to a preset minimum threshold.

7. An image compression performance optimization device based on EZW, characterized in that: The device comprises: A wavelet transform module, used for performing wavelet transform on the image data, and decomposing the image data into multi-level wavelet coefficients; A gradient quantization module, used for performing gradient quantization processing on the multi-level wavelet coefficients in different regions; A scanning module is used to perform main scanning and auxiliary scanning on the multi-level wavelet coefficients after the regional gradient quantization processing in the order from high frequency to low frequency based on the EZW algorithm; A coding judgment module, used for dynamically judging whether the descendant nodes of the parent node need to be coded based on the parasitic flag bits of the important coefficients during the main scanning process; The target module is used to generate and transmit coded data based on the main scanning result, the auxiliary scanning result and the judgment result.

8. A computer device, characterized in that: The device includes: a memory, a processor, and an EZW-based image compression performance optimization program stored in the memory and executable on the processor, wherein the EZW-based image compression performance optimization program is configured to implement the steps of the EZW-based image compression performance optimization method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores an EZW-based image compression performance optimization program, which, when executed by a processor, implements the steps of the EZW-based image compression performance optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the EZW-based image compression performance optimization method according to any one of claims 1 to 6 are implemented.

Images