237results about How to "Increase the compression ratio" patented technology

The invention is suitable for the technical field of video acquisition, and particularly relates to a video stream acquisition method based on deep learning, and the method comprises the steps: carrying out the video acquisition through a self-adaptive strategy, and obtaining real-time collected video data; performing parameter adjustment through edge calculation to obtain preprocessed video data; performing key frame selection on the preprocessed video data to obtain key frame data; performing inter-frame difference compression on the key frame data through the optimized variational auto-encoder model, and outputting video frame compression data; and when the original video needs to be recovered, analyzing the video compression data, reconstructing through the pre-trained neural network model to obtain a reconstructed video sequence, and generating a complete video according to the reconstructed video sequence. According to the method, the non-key frame is directly generated in the decoding process by using the deep learning model, so that the compression ratio can be remarkably improved, and the video playing continuity and the image quality, especially the performance in high-dynamic and complex scenes, can be improved.

The invention relates to the technical field of video compression processing. The invention provides a monitoring video compression processing method and device, and the method comprises the steps: extracting the video features of a to-be-processed image in a monitoring video in a target scene region through a video feature extraction model, and obtaining the feature vectors of a plurality of video features in different image regions; according to the obtained feature vectors of the various video features in the different image areas, performing following classification on the to-be-processed image to obtain classification results: image area classification, scene type classification and importance level classification; determining an initial compression ratio of different image areas in the to-be-processed image according to the classification result; decomposing the to-be-processed image into sub-images of different scales by adopting an image decomposition method based on wavelet transform, and determining an optimal decomposition layer number according to image quality; and determining a final compression ratio and a coding mode of different image areas according to the scale and content characteristics of the sub-images of the image to be processed. According to the method, the compression ratio is greatly improved on the premise that the image quality is not obviously reduced.

The invention discloses a method for performing efficient lossless text compression based on a large language model, which relates to the technical field of text compression and comprises the following steps of: preprocessing to-be-compressed text data to obtain an initial token sequence; selecting and loading a pre-trained big language model, analyzing the current context information by using the big language model, obtaining a probability value of each token, forming a probability distribution list, and recording a ranking sequence number of the current token; the context window is moved backwards by one token, and context information is updated until all the tokens are processed; and recording the ranking sequence numbers of all the tokens in sequence to form a sequence number list so as to obtain a final text compression result. According to the method, the compression rate is remarkably increased, the requirements for storage space and transmission bandwidth are reduced, the high processing speed is kept, and the method is suitable for real-time compression and decompression of large-scale text data.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

A foldable pushing chain type lifting platform belongs to the technical field of pushing chains and lifting platforms and comprises a meshed pushing chain system, an X-axis limiting mechanism and a Y-axis limiting mechanism, one side of the bottom of the meshed pushing chain system is fixedly connected with a base, the meshed pushing chain system outputs pushing chains upwards in the Z-axis direction, and the X-axis limiting mechanism and the Y-axis limiting mechanism are fixedly connected with the base. The top end of the pushing chain is fixedly connected with a top table used for bearing a lifted object, an X-axis limiting mechanism is further connected between the top table and the base, and the side, away from the meshing type pushing chain system, of the X-axis limiting mechanism is connected with a Y-axis limiting mechanism. The X-axis limiting mechanism is of a folding arm structure which only rotates around the X axis and limits the pushing chain and the top table to move in the X-axis direction. According to the invention, the pushing chain is used as a driving part, and the device has the advantages of high speed, large bearing capacity, high precision, long maintenance period, multi-point control, good stability, low initial height, large stroke, small occupied space and strong environmental adaptability.

The invention provides an intelligent control gas generator set and a control system, and relates to the technical field of gas power generation. The gas generator set comprises a compression ignition type internal combustion engine, a synchronous generator, an intelligent control box and a plurality of sensors. The intelligent control box comprises a display screen, a data memory, a central processing unit and a unit regulator; the central processing unit is used for receiving collected data of the sensors and processing the collected data to obtain adjustment parameters of the compression ignition type internal combustion engine; the unit regulator regulates the input air flow of the engine, the piston motion state and the rotating speeds of the engine and the synchronous generator according to the regulation parameters; the synchronous generator converts mechanical energy obtained by the engine into electric energy to be output, the operation efficiency and economical efficiency of the gas generator set are improved, emission is reduced, and environment-friendly power generation is achieved.

The invention provides a time sequence data compression-based database monitoring operation and maintenance method and system, which realize real-time monitoring of various index data of a database platform through an efficient data acquisition and compression technology. The system can efficiently collect various performance data generated in the operation process of the database, optimizes time series data through a compression algorithm, and reduces redundant storage of the data. In a data storage and management process, a primary differential sequence and a secondary differential sequence are calculated, so that the stored data volume is reduced; according to the method, a trend in an original data sequence is removed by using difference, so that a subsequent difference sequence is easier to compress, and meanwhile, for non-constant difference, by setting a dynamic threshold value and only recording a difference value greater than the threshold value, small changes are ignored and the compression ratio is improved on the premise of ensuring that important data information is not lost. And on the other hand, the regularity of the differential sequence is judged through Srelative, and a compression strategy can be selectively applied, so that proper compression methods can be specifically applied to time sequence data with different characteristics, and the processing efficiency is improved. Through the design, the storage and processing efficiency of the compressed data is improved, the system can decompress and analyze the data more quickly, and the running state of the database is monitored in real time.

The invention discloses a video data compression and reconstruction method, system and device and a medium, and relates to the technical field of video data compression, and the method comprises the steps: extracting a feature vector of a video frame in video data; performing sparse coding by taking the feature vector as a linear combination of a base vector in a dictionary to obtain a sparse coefficient of the video frame; coding the sparse coefficient of the video frame by using entropy coding to obtain a coded character string; reading the coded character string, searching each bit of the character string, moving the corresponding node according to the bit of the current character string, and adding the corresponding sparse coefficient feature vector into the decompressed sparse coefficient feature vector when the node is a leaf node; and reconstructing the sparse coefficient feature vector into an original feature vector by using inverse sparse coding, and outputting a reconstructed video frame. According to the method, the compression ratio far higher than that of a traditional compression standard is achieved, and the characteristic enables the higher compression ratio to be achieved on the premise that the video quality is guaranteed.

The invention relates to the technical field of intelligent garbage cans, in particular to an intelligent classification garbage can based on screw drive, which comprises a main body frame, the top of the main body frame is provided with a top cover, the top cover is provided with a garbage throwing port, and the front side surface of the main body frame is provided with a liquid crystal display controller; a spiral conveying mechanism is arranged below the garbage throwing opening and used for achieving garbage separation. A synchronous belt and a roller mechanism are arranged below the spiral conveying mechanism and are used for realizing garbage conveying; a storage mechanism for receiving garbage is arranged below the synchronous belt and the roller mechanism, and a rotary chassis mechanism is arranged below the storage mechanism; a battery is arranged on one side of the synchronous belt and the roller mechanism, and a compression mechanism is arranged on the other side of the synchronous belt and the roller mechanism and used for compressing the recyclable garbage. The garbage classification efficiency can be effectively improved, the intelligent degree of the garbage can is improved, and the difficulty of later garbage centralized classification treatment work is reduced.

The invention provides an efficient data compression storage algorithm based on a self-adaptive entropy optimization model. The efficient data compression storage algorithm comprises the steps of inputting a data set and parameters; performing initialization; carrying out data block processing; circularly processing each data block, calculating an entropy value, adjusting a weight, calculating a weighting frequency, updating a dynamic dictionary, coding and compressing, and updating the weight; judging whether unprocessed data blocks exist or not, and returning to the previous step when the unprocessed data blocks exist; when the unprocessed data blocks do not exist, continuing the next step; combining all the coding sequences; and outputting a result. Through the adaptive entropy optimization model and the progressive block optimization strategy, the compression scheme can be dynamically adjusted in real time according to the data entropy value, and the decompression speed and the storage efficiency are improved while the compression rate is ensured.

The invention relates to the technical field of mechanical transmission, and provides a connecting rod transmission mechanism and a mirror image engine. The connecting rod transmission mechanism comprises a power sliding block, a power connecting rod and two power pistons, the power connecting rod comprises a connecting rod base body and two linear rod parts, the linear rod parts extend in the first direction, the front end of each linear rod part is connected with one power piston, and the rear end of each linear rod part is fixedly connected with the connecting rod base body; the two linear rod parts are located on the two opposite sides of the connecting rod base body in the first direction, and the power sliding block is installed on the connecting rod base body in a sliding mode in the second direction. The linear rod part does not need to contain a bearing, rotation motion does not exist, the overall cross section is uniform or roughly uniform, standard sinusoidal motion / cosine motion is met, the size reduction design of the linear rod part and the air cylinder is facilitated, the compression ratio in the air cylinder is increased, the combustion efficiency is improved, the overall structure of the engine is more compact, and the needed engine cabin space is reduced. And the power-to-weight ratio and the acceleration performance of the vehicle can be improved.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device, and the method comprises the steps: S1, obtaining the working state of home equipment and the real-time data of environment information, and transmitting the real-time data to a central control platform; s2, preprocessing the real-time data to obtain preprocessed data; s3, analyzing the preprocessed data, and generating a corresponding linkage control instruction; s4, encoding the linkage control instruction to generate encoded data, and performing lossless compression on the encoded data by adopting a ZSTD compression algorithm to obtain a compressed code; s5, searching an optimal sending communication network by adopting a flora optimization algorithm, and sending the compressed code to the home equipment; and S6, the home equipment decompresses the compressed code by using an LZMA compression algorithm, and executes the linkage control instruction. According to the invention, the ZSTD compression algorithm and the flora optimization algorithm are adopted, the delay of data transmission and processing is significantly reduced, the overall response speed of the smart home system is improved, and the user experience is better.

According to the efficient data compression encryption and decryption method under the low-bandwidth condition, aiming at the characteristics of different data types, a specific compression algorithm is adopted to improve the compression ratio and the flexibility, and different encryption levels are adopted according to the data sensitivity degree to ensure the safety and the resource utilization efficiency. Meanwhile, by optimizing the time of all links such as compression, encryption, transmission, decryption and decompression, balance of the overall time is achieved, and therefore the overall efficiency of the system and the user experience are improved. According to the invention, some problems existing in data compression, encryption and decryption under the narrow bandwidth condition at present are effectively solved.

The utility model discloses a compression mechanism hydraulic system of a garbage collection vehicle and a working method of the compression mechanism hydraulic system in the field of hydraulic systems, which comprises a push plate oil cylinder, a scraper oil cylinder and a sliding plate oil cylinder, and a compression valve block, a back pressure valve block and a plurality of reversing valves are arranged corresponding to the push plate oil cylinder, the scraper oil cylinder and the sliding plate oil cylinder. The hydraulic oil tank supplies oil to the push plate oil cylinder, the scraper oil cylinder and the sliding plate oil cylinder through the duplex hydraulic pump, the reversing valves, the compression valve block and the back pressure valve block. The garbage compression device can improve the garbage compression rate and compression efficiency, plays a role in protecting the compression mechanism, and is convenient for personnel to maintain.

An internal combustion engine supplying gaseous fuel, in particular hydrogen, comprises, for each cylinder (1), a first intake duct (3A) and a second intake duct (3B) controlled by a first intake valve (VA) and a second intake valve (VB), respectively. In each operating cycle of the cylinder, first only the opening and closing movement of the first intake valve (VA) is activated while the second intake valve (VB) remains closed, and successively only the opening and closing movement of the second intake valve (VB) is activated while the first intake valve (VA) remains closed. According to the present invention, due to the cooling obtained during the opening of the first intake valve, gaseous fuel is directly supplied into the cylinder only from the moment after the first intake valve (VA) is closed and before the second intake valve (VB) is opened, thereby enabling regular combustion without a pre-ignition phenomenon or a tempering phenomenon.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention relates to a BNN-based hyperspectral image classification method, apparatus and device, and a memory. According to the method, a BNN-based hyperspectral image classification model is designed, excellent classification performance is realized while a CNN model is compressed, and a self-adaptive gradient scaling module based on a multi-scale pyramid compression attention mechanism is used as a full-precision real value bypass branch of a basic BNN network in a model training process. The method is used for flexibly adjusting the gradient in the back propagation process to reduce the gradient error, so that the binary neural network is better optimized without increasing the reasoning overhead, and meanwhile, the representation capability of the network is kept in forward propagation. Compared with an existing binaryzation or quantification method, the method has the advantages that the classification accuracy of the hyperspectral image is remarkably improved, and meanwhile, a high compression ratio is kept.

The invention discloses an analysis query method and device for a database, and the method comprises the steps: obtaining data corresponding to a query instruction from a pre-constructed preset storage structure through a preset index algorithm when the query instruction is received in a data query process of a database system, the preset storage structure is obtained by optimizing a storage structure design layer of a multi-version concurrency control mechanism, the preset storage structure is used for avoiding volume expansion caused by multi-version control and improving the performance of data analysis and query, and data is compressed through a data compression algorithm. And storing the compressed data in a preset storage structure in a column storage mode.

The present invention relates to the technical field of electronic digital data processing, and specifically discloses a hierarchical federated learning method of adaptive compression coding, the method comprising: hierarchical federated learning architecture construction, edge server matching, model generation performance judgment, compression rate matching and hierarchical federated learning architecture judgment, firstly, a hierarchical federated learning architecture is constructed so that each layer can make full use of its computing resources, an edge server corresponding to a terminal device is obtained by matching, each terminal device is used to generate an environmental data training model, a corresponding compression rate is obtained according to the network transmission performance of the edge server, the edge server receives the environmental data training model of each terminal device and performs adaptive compression coding and edge aggregation in sequence according to the corresponding compression rate, a cloud server receives the edge aggregation result and performs global aggregation, and finally judges whether the cloud server to which the hierarchical federated learning architecture belongs uploads the corresponding result of the global aggregation to an environmental resource scheduling cloud platform.

The invention provides a data compression method, device, equipment and system and a storage medium, which are used for compressing data and then storing the data to block equipment, and the block equipment comprises at least one block. The method comprises the following steps: acquiring a corresponding relationship between a block group and a compression algorithm; wherein the block group comprises one block or a plurality of continuous blocks, and the historical access frequency of the block group is in positive correlation with the compression speed of the compression algorithm; in response to a data write-in operation, executing the following compression process: acquiring an access address of the data write-in operation, and determining a target block where the access address is located; and compressing to-be-written data by using a target compression algorithm corresponding to the target block group to which the target block belongs according to the corresponding relationship between the block group and the compression algorithm.

The present invention relates to the technical field of video compression processing. A monitoring video compression processing method and device are provided, the method comprising: using a video feature extraction model to extract video features of an image to be processed in a monitoring video of a target scene area, and obtaining feature vectors of multiple video features in different image areas; performing the following classifications on the image to be processed according to the feature vectors of multiple video features in different image areas, and obtaining classification results: image area classification, scene type classification, and importance level classification; determining the initial compression ratio of different image areas in the image to be processed according to the classification results; using an image decomposition method based on wavelet transform to decompose the image to be processed into sub-images of different scales, and determining the optimal number of decomposition layers according to the image quality; determining the final compression ratio and encoding mode of different image areas according to the scale and content characteristics of the sub-images of the image to be processed. The present invention greatly improves the compression ratio without significantly reducing the image quality.

The invention relates to the technical field of video transmission, in particular to an agricultural product supply chain information management system, which comprises a video acquisition module, a video compression module and a video transmission module, and is characterized in that the gray scale range of a to-be-compressed image in a monitoring video is divided into a plurality of gray scale intervals; setting the gray value of each pixel point as the median of the gray interval to which each pixel point belongs, obtaining a target image, calculating the feature retention degree of the target image according to the gradient magnitude and the gradient direction of the pixel point with the feature maintained in the target image and the gradient magnitude and the gradient direction of the pixel point in the to-be-compressed image, and calculating the feature retention degree of the target image according to the feature retention degree. And taking the compression result of the target image with the minimum feature retention degree under different division modes as the compression result of the to-be-compressed image, and sending the compression result of all the to-be-compressed images to a background database for storage and processing. According to the method, the compression ratio is improved, the texture features of the image are reserved to the maximum extent, and an information management system is more efficient.

The invention belongs to the technical field of deep learning, and particularly discloses a model post-training quantification method and system based on Pareto optimization. Through the application, the weight and activation in the pre-trained floating point model are converted into integers with low bit width, the size and precision of the model are simultaneously optimized by utilizing the Pareto thought, and the comprehensive loss of multiple targets is minimum by constructing the joint loss function and calculating the Pareto plane, so that a Pareto optimal state is achieved; and then a balance point with the shortest actual reasoning time is screened out as a final quantification result, so that the improvement of the compression ratio of the model and the reasoning acceleration on resource-limited equipment are realized on the premise of ensuring the precision of the model.

The invention relates to the technical field of image compression, and discloses a semantic information-assisted task-oriented image compression method, system and device in order to solve the problems that the data volume of images to be transmitted is large and the airborne communication bandwidth is limited in unmanned aerial vehicle communication. The method comprises the steps that semantic segmentation is conducted on an original image according to a task knowledge base, a plurality of task related information units and background information units are obtained, and the task knowledge base comprises task backgrounds, task names, task requirements and task correlation scores; calculating the weight of each task related information unit according to the task correlation score and the data size of each task related information unit; according to the weight of each task-related information unit, bandwidth allocation is carried out on each task-related information unit and the background information unit; and according to a bandwidth allocation result, performing image compression on each task related information unit and the background information unit to obtain a multi-scale compressed image. By adopting the method, high-quality image transmission can be realized under the condition that the communication bandwidth is limited.

The invention relates to the field of data processing, in particular to a program management method and device, equipment and a medium, and the method comprises the steps: obtaining a program execution request of a target program; determining a target function corresponding to the program execution request according to the program execution request; determining a target function module compressed package corresponding to the target function from a plurality of function module compressed packages of the target program; wherein the function module compressed package corresponding to each function module of the target program is obtained by compression according to a compression ratio determined by the priority of the function module; and decompressing and loading the target function module compressed package so as to execute the target function. The functional modules are compressed and stored, and when the target function needs to be executed, only the compressed package of the target functional module is decompressed and loaded, so that the memory occupation is reduced, and the system performance is improved.

The invention belongs to the technical field of point cloud, and relates to a point cloud attribute coding method based on a graph attention network, which comprises the following steps: acquiring point cloud data, and carrying out block division on the point cloud data; performing detail level generation on the point cloud data subjected to block division, predicting point cloud attributes according to a detail level generation sequence, and inputting a neighbor point set with attribute information into a multi-head image attention layer for feature extraction to obtain a prediction residual error of a current point; encoding the prediction residual error of the current point to obtain an attribute code stream; inputting the attribute code stream into a decoding module to obtain a prediction residual error; searching a neighbor point set of unpredicted points in the prediction point set, and inputting the neighbor point set into the attention layer of the multi-head image to predict the current point; combining the predicted value with the decoded predicted residual error to obtain a reconstruction attribute; according to the method, efficient compression and accurate reconstruction of the point cloud data are realized in a layer-by-layer recursive predictive coding mode, and the method can be applied to a three-dimensional scene needing efficient data transmission and storage.

The invention relates to the technical field of battery diaphragms, in particular to a porous polymer microsphere and a preparation method and application thereof.The porous polymer microsphere is internally provided with a porous structure and comprises secondary particles and a coating layer coating the surfaces of the secondary particles, and the secondary particles are of a porous grid structure formed by polymerizing primary particles; the primary particles comprise a polyvinylidene fluoride polymer, and the coating layer comprises a polyacrylic acid polymer. The secondary particles have abundant pores, so that the liquid absorption and retention performance of the diaphragm can be improved, and meanwhile, the diaphragm has a relatively high compression ratio and is beneficial to improving the energy density of the battery. Compared with the polyacrylic acid polymer, the polyvinylidene fluoride polymer has higher crystallinity, and the polyacrylic acid polymer coats the surface of the secondary particle composed of the polyvinylidene fluoride polymer, so that the overall crystallinity of the porous polymer microsphere can be improved, and the adhesive property of the porous polymer microsphere is improved; and the bonding effect between the diaphragm and the pole piece can be improved.

The invention relates to an insulating flame-retardant automatic fire extinguishing paste and a preparation method thereof. The insulating flame-retardant fire extinguishing patch comprises a bottom film, an adhesive layer and an insulating flame-retardant layer, the insulating flame-retardant layer is prepared from the following components in parts by weight: 20 to 60 parts of perfluoro-2-methyl-3-pentanone microcapsules, 20 to 30 parts of polydimethylsiloxane, 0.1 to 5 parts of magnesium hydroxide, 0.1 to 5 parts of triphenyl phosphate, 0.5 to 5 parts of tributyl phosphate, 0.1 to 5 parts of tris (2-ethylhexyl) phosphate, 0.2 to 5 parts of tris (2, 3-dichloropropyl) phosphate, 0.2 to 5 parts of tetrabromobisphenol A epoxy resin, 0.5 to 3 parts of antimony trioxide and 0.1 to 5 parts of borate. 0.1 to 5 parts of dichlorobromomethane and 0.1 to 5 parts of trichlorobromomethane. The insulating flame-retardant fire extinguishing paste is easy to install, does not need maintenance, actively extinguishes fire when a fire occurs in the initial stage, can achieve the effect of multiple times of fire extinguishing, can effectively prevent reburning and fire expansion, and is safe, green and environmentally friendly.

The invention discloses a pulse compression device based on a multistage multi-pass cavity, which comprises a pulse laser and a plurality of compression modules arranged along a light path of the pulse laser, the compression module comprises a pulse laser spot matching module, a multi-pass cavity module, a light beam collimating lens and a pulse dispersion compensation module which are arranged along a light path of the pulse laser; according to the pulse compression device based on the multi-stage multi-pass cavity, the pulse width is continuously compressed by using the two-stage or multi-stage multi-pass cavity, and the pulse from hundred femtoseconds to picoseconds can be compressed to be below 10fs; the device is suitable for femtosecond or picosecond laser pulse beams with different pulse peak powers, the intensity of a nonlinear effect is adjusted by changing the thickness and position of a solid nonlinear medium or changing the type and pressure of a gas medium, and pulses with different energy and widths are optimized and debugged differently; the compression ratio can be effectively improved, and the problem that the compression ratio of a single-stage multi-way cavity is limited is solved.

The invention relates to the technical field of computers, and provides a potential space compression method, a potential space decompression method, a potential space compression device, a potential space decompression device, potential space compression equipment and a potential space decompression program product. Compressing the first potential variable through a neural network compression model to obtain a compressed second potential variable, and compressing the first potential variable through a neural network down-sampling model to obtain a down-sampled first potential variable; the arithmetic coding model is used for carrying out lossless Gaussian arithmetic coding on the down-sampled first potential variable to obtain a coded second potential variable; and the compressed second potential variable is transmitted to a decoding end, so that the decoding end decodes the second potential variable to obtain a decoded target image, the structural information of the data can be effectively utilized, efficient lossless compression can be realized, the first potential variable can be compressed more efficiently, the compression ratio of the image data is further improved, and the compression efficiency of the image data is improved. And image data compression cost is reduced.