Wiring-free Carplay reversing image wireless transmission method and system

Abstract

The invention discloses a wiring-free Carplay reversing image wireless transmission method and a wiring-free Carplay reversing image wireless transmission system, and belongs to the technical field of video coding. When a reversing mode is started, a wireless transmission link monitoring thread is started, a wireless transmission link performance index is acquired, and an acceptable target code rate of a current reversing image frame is given; after acquiring each frame of image, the reversing image acquisition equipment analyzes to obtain an initial block-level coding precision matrix, performs inter-frame smoothing and variation amplitude constraint on the coding precision of each spatial position in the matrix, and performs differential coding on each spatial coding block according to a final block-level coding precision matrix to obtain a final block-level coding precision matrix; the method comprises the following steps: generating a coding data stream adaptive to the bearing capacity of a current link, sending the coding data stream to a CarPlay terminal through a wireless transmission link, obtaining a monitoring result in a transmission process, dynamically and finely adjusting the target code rate and block-level precision distribution of a subsequent frame, and reducing the impact of queue backlog and frame-level loss of a sending end on the real-time performance and accuracy of a reversing image from the source.

Description

Technical Field

[0001] This invention relates to the field of video encoding technology, and in particular to a wireless transmission method and system for CarPlay reversing images without wiring. Background Technology

[0002] With the development of in-vehicle wireless communication technology, in order to reduce wiring harness costs, simplify vehicle installation processes, and improve the ease of installation of aftermarket systems, products that wirelessly transmit reversing camera signals to the vehicle's infotainment system or CarPlay display interface have emerged in recent years. CarPlay is essentially an in-vehicle connectivity system that also includes a display interface adapted to in-vehicle scenarios. For example, some solutions use wireless transmission links such as 2.4GHz / 5GHz WiFi or dedicated 5.8GHz digital image transmission modules to encode and compress the video signal collected by the rear camera and then wirelessly transmit it to the decoding terminal in the front cabin. The decoding terminal then connects to the vehicle's infotainment system or CarPlay box via a high-definition multimedia interface, a low-voltage differential signal interface, or a USB video input. These wireless solutions reduce the long-distance video wiring harness from the rear of the vehicle to the central control unit to a certain extent, making it easier to deploy quickly in the aftermarket and avoid areas where wiring is difficult to pass through in complex vehicle structures (such as SUVs and MPVs). However, since the in-vehicle radio environment often contains multiple wireless services such as the vehicle's built-in WiFi hotspot, mobile phone hotspot, Bluetooth audio, tire pressure monitoring system, and keyless entry, interference and competition within the shared frequency band will directly affect the stable transmission of reversing camera video.

[0003] In existing wireless transmission solutions for reversing cameras, to ensure basic image quality and real-time performance under limited bandwidth, video encoders based on standards such as Advanced Video Coding Standard (ADCS), High Efficiency Video Coding Standard (HEDS), or Joint Picture Experts Group (JPI) coding are commonly used. These encoders compress the frame-by-frame image sequence captured by the camera into a bitstream, which is then transmitted wirelessly. A typical process is as follows: the camera outputs the raw image, which is preprocessed by an image signal processor and then input into an encoding chip or system-on-a-chip (SoC). The encoder performs block partitioning, temporal / spatial prediction, transform quantization, and entropy coding to generate a compressed bitstream. The transmitting end slices and encapsulates the bitstream at the network or link layer and transmits it to the receiving end via User Datagram Protocol (UDP), Real-Time Transport Protocol (RTP), or proprietary wireless protocol. The receiving end then decapsulates and decodes the bitstream, finally sending the decoded image frames to the vehicle's infotainment system or CarPlay display interface. During this process, the encoder typically configures encoding parameters according to preset target bitrate, resolution, and frame rate to meet bitrate constraints on average. However, in most aftermarket or low-cost vehicle scenarios, the encoding end has limited ability to sense the wireless channel. The bit rate control is mostly adjusted based on the complexity of the current frame content and the fixed target bit rate, lacking deep linkage with the actual available bandwidth, latency and packet loss of the wireless transmission link.

[0004] The existing technology has the following technical problems: In real-world vehicle use, reversing often occurs in complex scenarios such as underground garages, narrow streets, rainy nights, backlighting, or strong reflections. In these situations, the images captured by the reversing camera contain a large amount of high-frequency information, including strong brightness contrast, local highlights, raindrop and noise textures, fine ground textures, and vehicle and pedestrian edges. This causes the statistical characteristics of the image to fluctuate dramatically in both time and space. Existing in-vehicle wireless reversing camera solutions generally adopt conventional bitrate control mechanisms based on H.264 / H.265. The encoding end mainly adjusts the quantization parameters dynamically based on the content complexity estimation of the current frame or several frames (such as residual energy, motion vector distribution, texture intensity, etc.) and the preset target bitrate. When the parking lot lights are flashing, oncoming headlights enter the frame, or noise increases in rainy nights, the complexity estimation module will judge these scenes as difficult to encode. In order to avoid severe mosaic and stripe artifacts, the bitrate controller often significantly reduces the quantization parameters and increases the output bitrate in a very short time. At this time, if the 2.4GHz / 5GHz frequency band in the vehicle is occupied by multiple sources of services such as vehicle hotspots, mobile phone hotspots, and Bluetooth devices, the actual available bandwidth and instantaneous packet loss rate of the wireless transmission link will fluctuate rapidly with the environment. The encoding end lacks direct awareness of these channel states and still increases the transmission bitrate according to the local complexity priority strategy. As a result, the buffer queue at the transmitting end accumulates rapidly, and the end-to-end transmission latency and jitter increase significantly. The image on the vehicle's infotainment system or CarPlay screen will show obvious stuttering, frame skipping, delayed display, or even block loss. Summary of the Invention

[0005] To address the technical problems of noticeable stuttering, frame skipping, delayed display, and even blocky image loss on in-vehicle or CarPlay screens in existing technologies, this invention provides a wireless CarPlay reversing image transmission method and system without wiring. The technical solution is as follows: On the one hand, a wireless transmission method for CarPlay reversing image without wiring is provided, the method comprising: Step 1: After the wireless CarPlay system starts in reversing mode, it initiates a wireless transmission link monitoring thread to collect wireless transmission link performance indicators and analyze the target bitrate of the reversing image frame. Step 2: The reversing image acquisition device acquires the reversing image frame and spatially divides it into several independent spatial coding blocks. Based on the target bitrate of the reversing image frame, it analyzes the initial block-level coding precision matrix corresponding to the reversing image frame. Inter-frame smoothing is performed on the coding precision of each spatial position in the initial block-level coding precision matrix to form the final block-level coding precision matrix. Step 3: Based on the final block-level coding precision matrix, differentiated coding processing is performed on each spatial coding block to generate a coded data stream adapted to the current wireless transmission link performance. The reversing image frame is transmitted to the CarPlay terminal via the wireless transmission link. During transmission, the transmission status of the coded data stream is monitored in real time, and the monitoring results are obtained. The coding parameters are dynamically adjusted based on the monitoring results to achieve adaptive dynamic optimization of the coding process in the wireless transmission scenario of the reversing image.

[0006] On the other hand, a wireless CarPlay reversing image transmission system without wiring is provided. This system includes: a target bitrate analysis module, an encoding precision smoothing module, and an encoding parameter adjustment module. The target bitrate analysis module is used to activate a wireless transmission link monitoring thread after the wireless CarPlay system enters reversing mode, collect wireless transmission link performance indicators, and analyze the target bitrate of the reversing image frame. The encoding precision smoothing module is used by the reversing image acquisition device to acquire the reversing image frame, spatially divide it into several independent spatial encoding blocks, and analyze the corresponding encoding parameters of the reversing image frame based on the target bitrate. The initial block-level coding precision matrix is ​​used to perform inter-frame smoothing operations on the coding precision of each spatial position on the initial block-level coding precision matrix to form the final block-level coding precision matrix. The coding parameter adjustment module is used to perform differentiated coding processing on each spatial coding block based on the final block-level coding precision matrix, generate a coding data stream adapted to the performance of the current wireless transmission link, and transmit the reversing image frame to the Carplay terminal through the wireless transmission link. During the transmission, the transmission status of the coding data stream is monitored in real time, the monitoring results are obtained, and the coding parameters are dynamically adjusted according to the monitoring results to achieve adaptive dynamic optimization of the coding process in the wireless transmission scenario of reversing image.

[0007] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention provides a wireless transmission method and system for CarPlay reversing images without wiring. When reversing mode is activated, the system first starts a wireless transmission link monitoring thread in parallel on the vehicle's side, continuously collecting wireless transmission link performance indicators, and based on this, provides an acceptable target bitrate for the current reversing image frame, so that subsequent encoding no longer simply relies on a fixed bitrate configuration. After acquiring each frame, the reversing image acquisition device divides the entire frame into several spatial coding blocks. An initial block-level coding precision matrix is ​​obtained by jointly calculating the target bitrate and the complexity of each block's content. Inter-frame smoothing and variation amplitude constraints are applied to the coding precision of each spatial position in the matrix, so that the block-level precision transitions slowly over time, reducing the introduction of new instantaneous bitrate peaks due to simultaneous drastic fluctuations in the link and the image. Based on this, each spatial coding block is differentiated according to the final block-level coding precision matrix to generate a coded data stream that matches the current link carrying capacity. This stream is then sent to the CarPlay terminal via a wireless transmission link. During the transmission process, monitoring results are acquired in real time, and the target bit rate and block-level precision allocation of subsequent frames are dynamically adjusted. This allows the coding side to continuously converge as the link state evolves, thereby reducing the impact of queue backlog and frame loss at the sending end on the real-time performance of the reversing image from the source.

[0008] 2. This invention, by performing inter-frame smoothing followed by neighborhood spatial smoothing, can suppress abrupt changes in block-level coding precision between adjacent frames in the temporal dimension, while simultaneously reducing excessive precision jumps between adjacent blocks in the spatial dimension. This makes the block-level coding precision distribution smoother and more continuous while meeting the link bit budget constraints. On the one hand, this avoids the problem of some block coding precision being abruptly increased when high-frequency textures suddenly increase or when headlight highlights instantly enter the frame, causing the instantaneous bit rate peak to exceed the wireless link's carrying capacity, thus reducing the risk of end-to-end latency and stuttering caused by the short-term overflow of the transmitter's buffer queue. On the other hand, it also reduces the disturbance of the statistical characteristics of the bitstream caused by drastic fluctuations in block-level precision in space, making the encoded output more consistent with the average available bandwidth of the link. This is beneficial for maintaining the stability of the reversing image in terms of subjective continuity and real-time performance in environments with multiple sources of interference, such as vehicle hotspots and mobile phone hotspots.

[0009] 3. Without changing the existing wireless transmission hardware architecture, this invention achieves closed-loop adaptive control of the real-time carrying capacity of the link and the time / spatial smoothing overhead on the encoding side: On the one hand, when the output delay of the current frame is detected to exceed the defined output delay, the target bit rate is automatically adjusted and reduced by adding a loss value to the piecewise linear function, so that the encoded bit amount of subsequent frames converges to the range that the wireless link can carry, avoiding buffer queuing, cumulative increase in output delay, and overall lag of the reversing image caused by the long-term high target bit rate; On the other hand, the total loss time introduced by inter-frame smoothing and neighborhood spatial smoothing is statistically analyzed within a preset monitoring window, and the first and second defined precision deviation values ​​are dynamically amplified based on the defined loss time, so that the system adaptively tightens the pursuit of local precision under the constraint of balancing image smoothness and acceptable delay, and suppresses the additional time overhead caused by excessive smoothing. Through the above-mentioned dual-layer adjustment, the present invention can coordinate and match the encoding complexity, bit rate allocation and wireless link latency constraints, and specifically alleviate the problems of increased reversing image output latency and insufficient real-time performance caused by the encoding end only adjusting parameters according to content complexity and ignoring smoothing loss and link effectiveness in the prior art. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a wireless transmission method for CarPlay reversing image without wiring, as provided in this application embodiment; Figure 2 A structural diagram of a wire-free CarPlay reversing image wireless transmission system provided in this application embodiment; Figure 3 A detailed flowchart of the reversing image frame analysis and transmission provided in this application embodiment; Figure 4 A detailed flowchart of dynamically adjusting encoding parameters provided in the embodiments of this application; Figure 5 A diagram illustrating the final block-level coding precision matrix formation process provided in this application embodiment. Detailed Implementation

[0012] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0013] To facilitate understanding of the embodiments of this application, the following points will be explained first: In this application, the use of prefixes such as "first" and "second" is solely for the purpose of distinguishing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0014] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0015] like Figure 1 The diagram shown is a flowchart of a wireless transmission method for CarPlay reversing image without wiring, provided in an embodiment of this application. The method includes the following steps: Step 1: After the wireless CarPlay system starts in reversing mode, it starts the wireless transmission link monitoring thread to collect wireless transmission link performance indicators and analyze the target bit rate of the reversing image frame.

[0016] CarPlay is an in-vehicle connectivity system that uses core technologies such as Bluetooth quick pairing and Wi-Fi high-speed transmission to project the functional data and visuals from Apple mobile phones onto a dedicated interface on the vehicle's screen. It features a reversing mode trigger mechanism; when the vehicle is in reverse gear, the system automatically switches to the reversing image display scene, acting as the receiving and display terminal for the reversing image signal. It receives the wirelessly transmitted and decoded video signal and outputs a visual representation. The wireless transmission link monitoring thread is a background real-time monitoring process initiated by the system or wireless receiving terminal (such as a CarPlay adapter) after the CarPlay system activates reversing mode. This thread periodically collects key performance indicators of the wireless transmission link (including but not limited to available bandwidth, instantaneous packet loss rate, transmission latency, signal strength, and channel interference), and performs real-time analysis and processing of the collected data based on a preset algorithm. Ultimately, it outputs a target bitrate for the reversing image frame adapted to the current link state, providing a basis for dynamic bitrate adjustment at the encoding end and achieving precise matching between the encoding strategy and the wireless channel state.

[0017] Step 2: The reversing image acquisition device acquires reversing image frames and divides them spatially to obtain several independent spatial coding blocks. Based on the target bit rate of the reversing image frames, the initial block-level coding precision matrix corresponding to the reversing image frames is analyzed. Inter-frame smoothing operation is performed on the coding precision of each spatial position on the initial block-level coding precision matrix to form the final block-level coding precision matrix.

[0018] Among them, reversing image acquisition equipment refers to the vehicle-mounted image acquisition terminal installed at the rear of the vehicle. Its core function is to capture visual information of the environment behind the vehicle in real time and output image or video data. Its hardware mainly includes image sensors, lens modules, image signal processors and data transmission interfaces. The typical product form is a vehicle-mounted reversing camera (including high-definition digital cameras, night vision enhanced cameras, etc.).

[0019] Step 3: Based on the final block-level coding precision matrix, perform differentiated coding processing on each spatial coding block to generate a coded data stream adapted to the performance of the current wireless transmission link. Transmit the reversing image frame to the Carplay terminal through the wireless transmission link. During the transmission process, monitor the transmission status of the coded data stream in real time, obtain the monitoring results, and dynamically adjust the coding parameters according to the monitoring results to achieve adaptive dynamic optimization of the coding process in the wireless transmission scenario of reversing image.

[0020] Specifically, the target bitrate of the reversing image frame is analyzed. The specific analysis process is as follows: after smoothing the performance indicators of the wireless transmission link through an exponential weighting algorithm, the processing result is marked as the link transmission quality coefficient.

[0021] In one example embodiment, the wireless transmission link performance metrics include link throughput, link transmission rate, and the signal strength received by the vehicle-mounted host. After normalizing the link throughput, link transmission rate, and the signal strength received by the vehicle-mounted host, these metrics are multiplied by their corresponding weighting coefficients. The result is labeled as the link transmission quality coefficient, such as: LTQC = DC. A+LTR B+VH C, where LTQC is the link transmission quality coefficient, DC is the normalized link throughput, LTR is the normalized link transmission rate, VH is the normalized on-board unit received signal strength, A is the weighting coefficient corresponding to the link throughput stored in the database, B is the weighting coefficient corresponding to the link transmission rate stored in the database, and C is the weighting coefficient corresponding to the on-board unit received signal strength stored in the database. The weighting coefficient is used to quantify the degree of influence of different parameters on the link transmission quality coefficient.

[0022] Link throughput refers to the amount of effective data successfully transmitted by the link per unit time, reflecting the actual data carrying capacity. It is calculated by statistically analyzing the total number of error-free data packets received within a fixed time period. Link transmission rate refers to the real-time data transmission speed of the link's physical layer, reflecting transmission efficiency. It is obtained by negotiating protocols (such as Wi-Fi 802.11 series) between the vehicle's host and the terminal's wireless communication module or by real-time monitoring of data frame transmission time. The signal strength received by the vehicle's host measures the strength of the wireless signal received by the host, obtained by directly collecting the signal power value (in dBm) through the host's wireless module. The relationship between these three factors and the link transmission quality coefficient is as follows: a stable and higher link transmission rate directly increases the amount of effective data transmitted per unit time, thus increasing the link throughput. This improves the stability of the link transmission rate, further resulting in a higher signal strength received by the vehicle's host, indicating stronger signal transmission stability and a lower probability of data packet loss and retransmission. Ultimately, all three factors jointly affect the link transmission quality coefficient.

[0023] The link transmission quality coefficient characterizes the real-time transmission performance of the wireless transmission link in the scenario of wireless transmission of CarPlay reversing image without wiring.

[0024] Using the link transmission quality coefficient as an index, the available bandwidth of the wireless transmission link is retrieved from the link transmission quality coefficient-available bandwidth mapping table in the database. By quantifying the abstract link transmission quality coefficient into available bandwidth through the mapping table, the upper layer coding no longer depends on a fixed bit rate or empirical threshold, but precisely constrains the total bit budget according to the current link carrying capacity, thus alleviating the problem of mismatch between the coding output and the instantaneous capacity of the wireless link, which leads to queuing delay and sudden packet loss.

[0025] Based on the available bandwidth of the wireless transmission link, the target bit rate of the reversing image frame is mapped by a piecewise linear function, and the variation range of the target bit rate between adjacent frames is limited.

[0026] Among them, limiting the target bitrate variation of adjacent frames means that the absolute value of the target bitrate difference between adjacent reversing image frames is less than the maximum value allowed by the target bitrate difference stored in the database. By constraining the jump of the target bitrate between adjacent frames, the buffer oscillation and instantaneous stuttering caused by drastic fluctuations in the encoded output are avoided, thereby improving the stability of the reversing image transmission process.

[0027] Optionally, the specific mapping process for the target bitrate of the reversing image frame is as follows: ; In the formula, B1 is the low bandwidth threshold, used to define the upper bound of the poor link state. avail When B1 is less than or equal to B2, the link is considered to be in a low bandwidth range. B2 is a high bandwidth threshold used to define the lower bound of a link's better state. avail When ≥B2, the link is considered to be in the high bandwidth range, α(Bavail α refers to the bandwidth utilization factor, which is the proportion of bandwidth that the encoder is allowed to use under the current available bandwidth conditions. It is used for subsequent calculations of the target bit rate. low As a low bandwidth utilization factor, when the available bandwidth of the link is in a poor state (low bandwidth range), the conservative bandwidth utilization ratio adopted by the encoding end is usually a small value to avoid queue backlog and excessive latency. α high To achieve high bandwidth utilization, when the available bandwidth of the link is in a good state (high bandwidth range), a higher bandwidth utilization ratio is adopted at the encoding end to improve image quality as much as possible within the limits of the link. When it is between B1 and B2, linear interpolation is used, from α... low Smooth transition to α high In order to avoid a sudden drop in the target bitrate.

[0028] ; R target B is the target bitrate for the reversing image frame. avail Rmax is the maximum allowable target bit rate corresponding to the available bandwidth of the wireless transmission link stored in the database. Through the above segmented linear mapping, the proportion of available bandwidth occupied by the encoder can be adaptively adjusted under different link states, providing a stable and continuous control quantity for the subsequent determination of the target bit rate.

[0029] Specifically, the initial block-level coding precision matrix corresponding to the reversing image frame is analyzed. The specific analysis process is as follows: obtain the total bit budget of the wireless transmission link, extract the single frame duration of the reversing image frame from the database, multiply the target bit rate of the reversing image frame by the single frame duration, subtract the proportion of protocol header, control signaling and forward error correction redundancy from the multiplication result, and the final result is the total bit budget of the current reversing image frame on the wireless transmission link, which represents the maximum number of bits allowed to be occupied by the frame without exceeding the target bit rate constraint. The single frame duration refers to the time interval between two consecutive reversing image frames output by the reversing image acquisition device.

[0030] The current reversing image frame is divided into several spatial coding blocks.

[0031] In one example embodiment, the current reversing image frame is divided into several spatial coding blocks, including: determining the target coding block size (e.g., 16×16 pixels or 32×32 pixels) according to the preset coding granularity of the image encoder; dividing the pixel matrix of the current reversing image frame into a grid according to the target coding block size in the row and column directions to obtain a set of regularly arranged spatial coding blocks; for residual pixel areas with edge regions that are less than the size of a complete coding block, they can be processed by zero padding, boundary expansion, or merging with adjacent coding blocks, so that the entire reversing image frame is covered by several discrete spatial coding blocks during encoding.

[0032] The content complexity of each spatial coding block is analyzed, and each spatial coding block is assigned a corresponding importance weight. The regional priority score of each spatial coding block is then calculated by weighted summation.

[0033] Content complexity refers to the average of the squared differences between the brightness values ​​of all pixels within a spatial coding block and the average brightness within the block. The larger the variance, the more drastic the brightness changes and the richer the texture within the coding block, and the higher its content complexity.

[0034] Importance weights are used to characterize the semantic importance of spatial coding blocks in a reversing scenario. Specifically, based on object detection, semantic segmentation, or pre-defined geometric region division results, each spatial coding block is first labeled with a content type tag, such as the vehicle rear region, the region near the reversing guide lines, the detected pedestrian / obstacle region, and the background region. Then, using the content type tag as an index, the corresponding importance weight value is retrieved from a pre-established content type-importance weight mapping table in the database and assigned to the current coding block. Through this mapping table lookup mechanism, coding blocks in different semantic regions have configurable priority differences in subsequent bit allocation and coding precision control.

[0035] Optionally, the specific regional priority scoring is as follows: ; In the formula, RPS is the regional priority score, CP is the normalized content complexity, IW is the normalized importance weight, D1 is the weight coefficient corresponding to the content complexity stored in the database, and D2 is the weight coefficient corresponding to the importance weight stored in the database. The weight coefficient is used to quantify the influence of different parameters on the regional priority score. Content complexity reflects the information density of spatial coding blocks (e.g., the complexity of blocks with rich edges and details in a reversing image is higher). The higher the content complexity, the greater the importance weight of the coding block will be assigned (because complex areas contain key visual information, and transmission quality needs to be prioritized). The regional priority score is obtained by weighting and summing the content complexity and corresponding importance weight of each spatial coding block. Content complexity indirectly positively affects the score by influencing the value of the importance weight, while the importance weight directly amplifies or reduces the proportion of the corresponding complex block in the score. Ultimately, the two together determine the level of the regional priority score.

[0036] Based on the regional priority scores of each spatial coding block, the total bit budget of the current wireless transmission link is allocated to obtain the block-level bit budget of each spatial coding block. Specifically, the sum of the regional priority scores of all spatial coding blocks is calculated, the ratio of the score of a single coding block to the total score (i.e., the priority ratio) is obtained, and the total bit budget of the current wireless transmission link is multiplied by this ratio to obtain the block-level bit budget of the corresponding coding block. That is, the block-level bit budget = total bit budget × (single block regional priority score ÷ sum of all block scores).

[0037] The block-level bit budget of each spatial coding block is mapped to a preset matrix level index set to determine the block-level coding precision used by each spatial coding block.

[0038] A matrix level index set is a predefined, structured set containing multiple ordered level indices. Essentially, it's a mapping table between coding precision levels and index identifiers. Its core function is to provide a standardized matching basis for block-level bit budgets. By mapping continuously changing bit budget values ​​to discrete, predefined level indices, it achieves standardized selection of coding precision (avoiding chaotic precision configuration).

[0039] Block-level coding precision is a core metric that quantifies the coding quality and algorithmic complexity of a single spatial coding block. It uses a precision quantization value (such as integer level or floating-point coefficient) as a unified identifier, and this quantization value is bound to a set of preset coding configuration parameters (resolution, compression ratio, prediction mode, etc.). The core logic is: the higher the precision quantization value, the more complex the corresponding coding configuration, and the higher the fidelity of the transmitted block data; conversely, the coding configuration is simpler to save bit resources.

[0040] In one example embodiment, taking the CarPlay reversing image transmission scenario without wiring as an example, the system's preset matrix level index set includes five ordered levels from Index-1 to Index-5. Each level corresponds to a fixed bit budget range, a unique block-level coding precision quantization value, and bound coding configuration parameters (such as Index-3 corresponding to a 3.5-6.0Mbps bit budget range, a block-level coding precision quantization value of 3, and bound to a 128×128 pixel resolution and a 10:1 compression ratio, etc.). After bit budget allocation, the block-level bit budgets of block A (close-up detail area of ​​the vehicle body) and block B (road edge transition area) are 5.8Mbps and 3.6Mbps, respectively. The system maps the above bit budgets one by one to the matrix level index set, determines that both block A and block B fall into the range corresponding to Index-3, obtains the block-level coding precision of 3 and the corresponding coding configuration, and thus completes the determination of the block-level coding precision of each spatial coding block in sequence.

[0041] According to the spatial position of each spatial coding block in the current reversing image frame, the corresponding original matrix level is filled into a two-dimensional grid consistent with the coding block division, so as to obtain an initial block-level coding precision matrix that reflects the block-level coding precision of each spatial coding block in terms of spatial distribution.

[0042] In one example embodiment, the spatial position of each spatial coding block in the current reversing image frame is directly obtained through the image pixel coordinate system. A two-dimensional pixel coordinate system is established with the top-left corner of the image frame as the origin, the horizontal axis representing the horizontal pixel direction (X-axis), and the vertical axis representing the vertical pixel direction (Y-axis). The position of each spatial coding block is defined by the pixel coordinates (X-start value, X-end value) and (Y-start value, Y-end value) of its top-left corner vertex. Combined with the block's own pixel size (e.g., a 16×16 pixel macroblock), the unique spatial region of the coding block in the entire image frame can be clearly identified. The initial block-level coding precision matrix is ​​constructed by establishing a row and column structure based on the coding block division method, with the number of rows in the matrix... The number of coding blocks in the vertical and horizontal directions of the image frame is matched with the number of columns (for example, when a 1080-pixel high image is divided into coding blocks of 16 pixels high, there are 67 blocks in the vertical direction, i.e., the matrix has 67 rows; when a 1920-pixel wide image is divided into 16-pixel wide images, there are 120 blocks in the horizontal direction, i.e., the matrix has 120 columns). Then, the original matrix level corresponding to each spatial coding block is filled into the corresponding row and column positions of the matrix according to its spatial order from top to bottom and from left to right in the image frame, so that the row and column distribution of the matrix is ​​completely aligned with the spatial distribution of the coding blocks. The final matrix can intuitively map the spatial position of each coding block through the row and column positions, and its element values ​​reflect the block-level coding precision of the corresponding coding block.

[0043] In this embodiment, the core value of quantizing block-level coding precision lies in providing standardized and computable quantization indicators for matrix smoothing: In complex scenarios, the statistical characteristics of reversing images fluctuate drastically in time and space, and the wireless channel bandwidth and packet loss rate change dynamically. Unquantized coding precision lacks a unified scheduling benchmark and cannot achieve precision balance across blocks and frames. By quantizing, coding precision is transformed into computable values ​​(such as level indexes), and the precision differences of each spatial coding block can be dynamically harmonized by the matrix smoothing algorithm, avoiding the blind increase in bit rate caused by local high-frequency information. At the same time, it adapts to channel state fluctuations and ensures the coordinated adaptation of coding strategy and transmission link.

[0044] Specifically, the final block-level coding precision matrix is ​​formed through the following process: inter-frame smoothing is performed on the coding precision at each spatial location of the initial block-level coding precision matrix; after the inter-frame smoothing is completed, the initial block-level coding precision matrix is ​​updated; neighborhood spatial smoothing is performed on the coding precision at each spatial location of the updated initial block-level coding precision matrix; after the neighborhood spatial smoothing is completed, the initial block-level coding precision matrix is ​​updated, and the updated initial block-level coding precision matrix is ​​marked as the final block-level coding precision matrix.

[0045] Inter-frame smoothing can mitigate the drastic temporal fluctuations in the statistical characteristics of reversing images in complex scenarios, preventing frequent abrupt changes in coding precision caused by local high-frequency information in a single frame (such as bright spots and raindrop noise), thereby preventing large fluctuations in bitrate in a short period of time. Neighborhood spatial smoothing can balance the precision differences between spatial coding blocks, avoiding image fragmentation caused by abrupt changes in precision between adjacent blocks. At the same time, through cross-frame and cross-space precision harmonization, the coding strategy is more adaptable to the dynamic changes in wireless channel bandwidth and packet loss rate, reducing backlog in the sender's buffer queue, effectively reducing transmission latency and jitter. It solves problems such as image stuttering, frame skipping, delayed display, and block loss in conventional solutions from both temporal and spatial dimensions, ensuring the stability and continuity of reversing image transmission in complex environments.

[0046] Furthermore, inter-frame smoothing is performed on the coding precision of each spatial location on the initial block-level coding precision matrix. Specifically, for a certain spatial location on the initial block-level coding precision matrix, the actual block-level coding precision used at that spatial location in the previous frame is obtained from the data log as the historical precision LH.

[0047] The block-level coding precision LD corresponding to the current reversing image frame at this spatial location is weighted by the historical precision LH according to the preset smoothing coefficient β. The weighted result is marked as the temporary block-level coding precision LT at this spatial location, that is, LT=LD×β+LH×(1-β), where LT is an integer rounded up.

[0048] The encoding precision is in integer form. The core block-level encoding precision has been quantized into discrete integer levels (such as levels 1-5) through a preset matrix level index set. Each level is uniquely bound to encoding configuration parameters (resolution, compression ratio, etc.). The integer form ensures accurate mapping with the matrix index, providing a standardized calculation benchmark for subsequent inter-frame / spatial smoothing and encoding parameter calls, avoiding index matching confusion or configuration ambiguity caused by non-integers. The rounding up rule is adopted to prioritize the encoding quality of key areas in the current frame in complex scenarios (such as those containing high-frequency information such as high-brightness spots and noise). When the weighted calculation result is not an integer, rounding up can bring the encoding precision closer to a higher level, avoiding insufficient precision caused by rounding down, and ensuring the effective preservation of detailed information in the area. At the same time, combined with the constraint of the first defined precision deviation value, it can avoid excessive leaps in precision and bit rate while ensuring quality, achieving a balance between quality and link adaptation.

[0049] The smoothing coefficient is used to control the proportion of the current frame block-level coding precision and the historical precision of the previous frame in the weighted result.

[0050] Obtain the precision deviation value between the temporary block-level coding precision and the historical precision, i.e. .

[0051] The first defined precision deviation value JL is extracted from the database. This first defined precision deviation value is a pre-set inter-frame coding precision mutation constraint threshold in the database. Its core function is to limit the upper limit of the deviation between the temporary precision obtained by weighting the current frame block-level coding precision and the historical precision of the previous frame, and the historical precision. When the calculated precision deviation value... When the deviation is greater than JL, the system will truncate the deviation (i.e., force the deviation value to be limited to the first defined precision deviation value, and simultaneously adjust the temporary precision to a reasonable value that meets the constraints), so as to avoid a large jump in single-frame coding precision due to local high-frequency information (such as high light spots and raindrop noise) in complex scenarios, thereby preventing the bit rate from rising sharply beyond the actual carrying capacity of the wireless transmission link, ensuring a smooth transition of the inter-frame coding strategy, and ensuring dynamic adaptation of the coding output to the link bandwidth.

[0052] The precision deviation between the temporary block-level coding precision and the historical precision is limited based on the first defined precision deviation value. The temporary block-level coding precision of each spatial location is updated sequentially to obtain an initial block-level coding precision matrix that satisfies the time continuity constraint.

[0053] Assuming the wireless CarPlay reversing camera system has a preset smoothing coefficient β=0.3 (emphasizing historical accuracy stability) and a first-boundary accuracy deviation value JL=1 (maximum inter-frame allowable deviation for integer-level accuracy), the encoding accuracy quantization value is an integer level from 1 to 5. At a certain spatial location, the historical accuracy of a frame is LH=2 (integer level). In the current frame, due to local highlights, the block-level encoding accuracy is LD=4 (integer level). According to the formula LT=4×0.3+2×(1-0.3)=2.6, after rounding, the temporary accuracy LT=3 (integer level). Its deviation from LH is 3-2=1, which equals JL=1, satisfying the constraint. Therefore, the accuracy at this location is directly updated to 3. At another spatial location, LH=1 and LD=4, the calculated LT=4×0.3+1×0.7=1.9. After rounding, LT=2, with a deviation of 2. -1=1≤JL, normal update; if LH=2 and LD=5 at a certain position, LT=5×0.3+2×0.7=2.9 is calculated, and after rounding, LT=3, the deviation value 3-2=1≤JL, and truncation is not triggered; if LH=3 and LD=5 at a certain position, LT=5×0.3+3×0.7=3.6 is calculated, and after rounding, LT=4, the deviation value 4-3=1≤JL. All spatial positions are updated in sequence to form an initial block-level coding precision matrix that satisfies the temporal continuity constraint, avoiding drastic fluctuations in integer precision and code rate.

[0054] Furthermore, forming the final block-level coding precision matrix also includes performing neighborhood spatial smoothing on the coding precision of each spatial position in the initial block-level coding precision matrix. The specific operation process is as follows: the second defined precision deviation value JL2 from the database is used as a key threshold to constrain the precision difference between spatial coding blocks. Its core function is to limit the maximum allowable deviation between a certain spatial position in the initial block-level coding precision matrix and the temporary block-level coding precision of adjacent spatial positions.

[0055] On the initial block-level coding precision matrix, analyze the precision deviation between the temporary block-level coding precision LTA at a certain spatial location and the temporary block-level coding precision LTB at adjacent spatial locations. ; If the accuracy deviation between the temporary block-level coding accuracy of a spatial location and that of an adjacent spatial location is higher than the second defined accuracy deviation value, then the temporary block-level coding accuracy level of that spatial location is corrected to converge to the average temporary block-level coding accuracy of the neighborhood, and the temporary block-level coding accuracy level of that spatial location is updated.

[0056] First, the system's preset neighborhood range is defined (e.g., a 4-neighborhood includes the upper, lower, left, and right adjacent blocks of the current coding block, while an 8-neighborhood additionally includes diagonal adjacent blocks, and the specific execution follows the preset rules). Then, the temporary block-level coding precision levels (all integers) of all adjacent blocks are extracted and their arithmetic mean is calculated, with one decimal place retained. Next, the absolute value of the difference between the current block's temporary precision level and this average value is calculated. If this absolute value exceeds the second-bound precision deviation value, it is corrected according to the "nearest integer convergence" rule: when the current block's precision level is higher than the average value, it is lowered to the largest integer that is "less than or equal to the average value and the absolute value of the difference from the average value does not exceed the second-bound precision deviation value"; when the current block's precision level is lower than the average value, it is raised to the smallest integer that is "greater than or equal to the average value and the absolute value of the difference from the average value does not exceed the second-bound precision deviation value". If the corrected precision level of the current block still exceeds the second-bound precision deviation value from the precision deviation of any adjacent block, the aforementioned average value is replaced with the median of the precision levels of all adjacent blocks, and the above correction operation is repeated until the precision deviation of the current block from all adjacent blocks meets the constraints, thus completing the correction and update of the current block's precision level.

[0057] The temporary block-level coding precision level of each spatial location is updated sequentially to form an initial block-level coding precision matrix that simultaneously satisfies the spatial continuity constraint.

[0058] Assuming that in a wire-free CarPlay reversing camera system, the database presets a second definition precision deviation value JL2=1 (corresponding to the maximum allowable spatial deviation of the coding precision quantization value), and a certain region in the initial block-level coding precision matrix contains 5 adjacent spatial coding blocks (center block P0 and the upper, lower, left, and right adjacent blocks P1~P4), after inter-frame smoothing, the temporary coding precision quantization values ​​of each block are as follows: P0=3, P1=3, P2=3, P3=1, P4=3. Analysis of spatial deviation reveals that the precision deviation between the central block P0 and its neighboring block P3 is 3-1=2, which is higher than the second-bound precision deviation value JL2=1, triggering the correction mechanism. The system calculates the average precision of the neighboring blocks (P1-P4) as (3+3+1+3) / 4=2.5, and corrects the temporary coding precision of P0 to a reasonable quantization value of 2 that converges to this average value (since the precision quantization value is an integer). After correction, the deviation between P0 and all neighboring blocks is ≤1, satisfying the spatial continuity constraint. Following this logic, the temporary coding precision of all spatial positions in the matrix is ​​checked and corrected sequentially, ultimately forming an initial block-level coding precision matrix with a smooth transition in spatial precision.

[0059] Figure 3This is a detailed flowchart of the reversing image frame analysis and transmission provided in this application embodiment. After the wireless CarPlay system starts the reversing mode, it starts the wireless transmission link monitoring thread, collects the performance indicators of the wireless transmission link, and analyzes the target bitrate of the reversing image frame. Based on the target bitrate of the reversing image frame, it analyzes the initial block-level coding precision matrix corresponding to the reversing image frame, performs a smoothing operation on the coding precision of each spatial position on the initial block-level coding precision matrix to form the final block-level coding precision matrix, performs differentiated coding processing on each spatial coding block based on the final block-level coding precision matrix, generates a coded data stream adapted to the current wireless transmission link performance, and transmits the reversing image frame to the CarPlay terminal through the wireless transmission link. During the transmission process, the transmission status of the coded data stream is monitored in real time, the monitoring results are obtained, and the coding parameters are dynamically adjusted according to the monitoring results.

[0060] The monitoring results are a core set of data reflecting transmission quality and link adaptability.

[0061] In one example embodiment, differentiated encoding processing is performed on each spatial coding block to generate an encoded data stream adapted to the performance of the current wireless transmission link. Specifically, assuming that in the final block-level encoding precision matrix generated by the wireless CarPlay reversing image system, the encoding precision level of the close-range detail area (key area) of the vehicle body is 4, the road edge area is 3, and the distant background area is 1, and the available bandwidth of the current wireless transmission link is detected to be stable at 8Mbps and the packet loss rate is less than 1%, the system performs differentiated encoding on each spatial coding block according to this matrix: the detail area with precision level 4 adopts a resolution of 256×256 pixels, a low compression ratio of 5:1, and a multi-reference frame prediction strategy to maximize the preservation of edge details of pedestrians and obstacles around the vehicle body; the road edge area with precision level 3 adopts a resolution of 128×128 pixels and a compression ratio of 10:1 to balance clarity and bit consumption; the background area with precision level 1 adopts a resolution of 32×32 pixels and a high compression ratio of 40:1 to significantly save bit resources. The total bit rate of the finally generated encoded data stream is controlled at 7.2Mbps, which is fully adapted to the link performance and presents a clear and smooth reversing image on the vehicle screen.

[0062] Specifically, the coding parameters are dynamically adjusted based on the monitoring results. The adjustment process is as follows: extract the actual output delay duration of the current reversing image frame from the monitoring results, and extract the defined output delay duration from the database, representing the maximum allowable output delay duration.

[0063] The encoding parameters include a piecewise linear function, a first bounding precision deviation value, and a second bounding precision deviation value.

[0064] Output delay duration refers to the time interval from when the reversing camera system collects real-time scene data (such as a camera capturing the environment behind the vehicle) to when the corresponding image frame of that scene is displayed on the in-vehicle display device (such as the central control screen).

[0065] The actual output delay of the current reversing image frame is compared with the defined output delay. If the actual output delay of the current reversing image frame is not higher than the defined output delay, the reversing image encoding process is continuously monitored. If the actual output delay of the current reversing image frame is higher than the defined output delay, a loss value is added to the piecewise linear function to reduce the target bit rate.

[0066] Adding a loss value to the piecewise linear function involves retrieving the corresponding loss value from the actual output delay duration-loss value mapping table stored in the database, using the actual output delay duration as an index, and then inserting it into the piecewise linear function, as detailed below: ; F is the loss value, used to represent the reduction in bandwidth utilization. By comparing the actual output delay with the defined output delay, it can accurately capture the transmission processing timeout problem of a single frame of image, avoiding the time difference between the image and the actual vehicle condition exceeding the safety threshold due to accumulated delay (e.g., a delay of more than 0.5 seconds when reversing into a parking space can easily cause distance misjudgment). If the actual output delay of the current reversing image frame is not higher than the defined output delay, it can maintain the matching state of image clarity and transmission efficiency at the current bitrate, preventing image distortion caused by indiscriminate bitrate reduction. If the actual output delay of the current reversing image frame is higher than the defined output delay, the loss value is dynamically increased and the target bitrate is reduced through a piecewise linear function, which can quickly reduce the data encoding complexity and transmission bandwidth occupation, so that the output delay falls back to the safe range within 1-2 frames. This effectively solves the contradiction of high processing load causing delay spikes under high bitrate, and ultimately achieves effective image presentation under low latency, directly reducing the risk of image lag.

[0067] The reversing image encoding process is continuously monitored. Within the monitoring window preset by the technicians, the duration of inter-frame smoothing operation and the total duration of neighborhood spatial smoothing operation are obtained from the vehicle log and accumulated. The summary result is marked as the total loss duration.

[0068] The encoding parameters are deeply adjusted based on the total loss time.

[0069] Furthermore, the coding parameters are deeply adjusted based on the total loss time. The adjustment process is as follows: The total loss time is compared with the defined loss time stored in the database; the defined loss time refers to the minimum allowed total loss time.

[0070] If the total loss time does not exceed the defined loss time, the reversing image encoding process will be continuously monitored.

[0071] If the total loss time is higher than the defined loss time, then the first and second defined accuracy deviation values ​​are increased. Using the total loss time as an index, the database is queried for the increment of the first and second defined accuracy deviation values ​​corresponding to the total loss time. The current first defined accuracy deviation value is added to the first defined accuracy deviation value increment, and the current second defined accuracy deviation value is added to the second defined accuracy deviation value increment.

[0072] Increasing the first and second precision deviation values ​​can directly relax the threshold for precision verification during the encoding process. On the one hand, this reduces repetitive calculations and parameter fine-tuning operations triggered by precision deviations approaching the threshold, shortens the verification time for single-frame encoding, and effectively reduces time loss in the encoding process. On the other hand, expanding the precision deviation threshold can reduce the computational complexity under high-precision constraints in piecewise linear functions, making iterative adjustments of encoding parameters more efficient and avoiding computational dead loops due to excessive pursuit of precision. This quickly brings the total loss time back within the defined range, directly resolving the technical contradiction of total loss timeout and disordered image output rhythm caused by precision verification redundancy. It ensures that the time cost of the reversing image encoding process is controllable and provides a time dimension guarantee for real-time output of stable reversing images.

[0073] Furthermore, to achieve adaptive dynamic optimization of the encoding process in the scenario of wireless transmission of reversing images, it is also necessary to determine whether to update the encoding parameters. The specific determination process is as follows: if the encoding parameters are in an unupdateable state, it is determined that the encoding parameters will not be updated; if the encoding parameters are in an updatable state, the total bitrate deviation value of this wireless transmission of reversing images is obtained. The total bitrate deviation value refers to the cumulative statistical value of all deviation data between the real-time bitrate sequence actually output by the encoding stage and the target bitrate preset by the system during the entire process of wireless transmission of reversing images (including the sum of the absolute values ​​of deviations where the real-time bitrate is higher or lower than the target bitrate in a single or multiple transmissions).

[0074] The terms "updatable state" and "non-updatable state" refer to the update mechanism of the encoding parameters for reversing camera wireless transmission. An updatable state means the encoding parameters are not within the initial locking period, i.e., after a fixed time preset by the system (e.g., after three consecutive reversing scenarios). At this point, the parameters have completed the adaptation for the current cycle and are ready to be adjusted based on new transmitted data (e.g., the total bitrate deviation). A non-updatable state means the encoding parameters are within the initial locking period. The parameters are initialized each time reversing is initiated, but the system restricts them from being adjusted for a short period (e.g., during a single reversing operation or between two consecutive reversing operations) to avoid frequent parameter changes that could compromise the stability of the encoding process. This state division prevents parameters from being blindly modified due to temporary fluctuations in a single reversing operation, while ensuring that parameters are accurately updated after accumulating sufficient transmitted data, adapting to the dynamic changes in bitrate, latency, and other indicators in wireless transmission scenarios.

[0075] Extract the defined total bitrate deviation value from the database and compare it with the total bitrate deviation value of this reversing image wireless transmission to define the total bitrate deviation value, which refers to the maximum allowed value of the total bitrate deviation value.

[0076] If the total bitrate deviation of the current wireless transmission of the reversing image is higher than the defined total bitrate deviation, it is determined that the encoding parameters will not be updated; if the total bitrate deviation of the current wireless transmission of the reversing image is not higher than the defined total bitrate deviation, it is determined that the encoding parameters will be updated, and the encoding parameters that are finally fixed during the current wireless transmission of the reversing image will be overwritten with the historical encoding parameters in the database.

[0077] The total bitrate deviation value can accurately quantify the overall deviation between the actual bitrate and the target bitrate during the reversing process, avoiding parameter misadjustment caused by single frame or instantaneous bitrate fluctuations. By statistically analyzing the cumulative deviation, it can objectively reflect the comprehensive impact of the wireless transmission environment (such as signal interference and bandwidth fluctuations) and the complexity of the image content (such as the amount of detail in the rear scene) on the bitrate output, providing real and comprehensive data support for updating encoding parameters. Updating encoding parameters based on this value can make the parameters adapt to the long-term patterns of the current wireless transmission scenario rather than temporary fluctuations, effectively solving problems such as soaring transmission latency, image stuttering and distortion, and bandwidth waste caused by fixed bitrates or frequent fine-tuning of parameters. This ensures that the bitrate of the encoded output is more in line with the actual transmission requirements in subsequent wireless transmission of reversing images, achieving a dynamic balance between transmission stability, image quality, and bandwidth utilization, and improving the reliability and adaptability of wireless transmission of reversing images.

[0078] Figure 4This document provides a detailed flowchart of the dynamic adjustment of encoding parameters in an embodiment of this application. During transmission, the transmission status of the encoded data stream is monitored in real time, and monitoring results are obtained. Encoding parameters are dynamically adjusted based on these results. The actual output delay of the current reversing image frame is extracted from the monitoring results, and a defined output delay is extracted from the database. The actual output delay of the current reversing image frame is compared with the defined output delay. If the actual output delay of the current reversing image frame is not higher than the defined output delay, the reversing image encoding process is continuously monitored. If the actual output delay of the current reversing image frame is higher than the defined output delay, the process continues. The loss value is added to the piecewise linear function to reduce the target bit rate. The reversing image encoding process is continuously monitored. Within a preset monitoring window, the duration of inter-frame smoothing operation and the total duration of neighborhood spatial smoothing operation are obtained and accumulated. The summation result is marked as the total loss duration. The encoding parameters are deeply adjusted based on the total loss duration. The total loss duration is compared with the defined loss duration stored in the database. If the total loss duration is not higher than the defined loss duration, the reversing image encoding process is continuously monitored. If the total loss duration is higher than the defined loss duration, the first defined accuracy deviation value and the second defined accuracy deviation value are increased.

[0079] Figure 5 This diagram illustrates the final block-level coding precision matrix formation process provided in this application embodiment. From left to right, it shows the block-level coding precision matrices for three stages: L on the left... D(t) This is the initial block-level coding precision matrix for the current reversing image frame. Each element is an integer precision level directly calculated based on the content complexity and importance weight of each spatial coding block in this frame; the L above... H(t−1) The accuracy matrix actually used in the previous frame serves as the historical accuracy for each spatial location, in relation to L. D(t) With L H(t−1) After weighting by a preset smoothing coefficient and constraining the step size by combining it with the first defined accuracy deviation value, the intermediate L is obtained. time(t) The time-smoothed matrix, where the dark-marked cells represent the spatial locations where the precision level was updated during the inter-frame smoothing operation; subsequently, L... time(t) Based on this, a second definition precision deviation value is introduced. For blocks with excessively large differences from the average precision of their neighborhood, a neighborhood space smoothing correction is performed to obtain L on the right side. final(t) The final block-level coding precision matrix, with dark cells in the figure representing the spatial locations where the precision level was further adjusted during the neighborhood space smoothing process.

[0080] It should be noted that the examples in this embodiment are for reference only. Their core purpose is to clearly illustrate the matching logic between matrix rows and columns and the distribution of coding blocks, and they are not limitations on image resolution, coding block size, or matrix specifications.

[0081] like Figure 2 The diagram shown is a schematic of a wireless transmission system for CarPlay reversing images that does not require wiring, according to an embodiment of this application. The system includes a target bitrate analysis module, an encoding accuracy smoothing module, and an encoding parameter adjustment module.

[0082] A wireless CarPlay reversing image transmission system, also including a database, is used to store parameters involved in the wireless CarPlay reversing image transmission system. The database follows the principles of "clear classification, adaptable retrieval, and support for updates": it is structured and classified into basic configuration parameters (such as CarPlay protocol adaptation parameters and initial encoding algorithm configuration), dynamic operating parameters (such as the total bitrate deviation and total loss time for this reversing operation), threshold definition parameters (such as the threshold loss time and output delay threshold), and historical statistical parameters (such as historical optimal parameter configuration records). Unique identifiers and associated indexes are designed for each type of parameter, and the storage format and update rules are clearly defined (dynamic parameters are written according to a single reversing cycle, threshold parameters support upgrade updates, and historical parameters are archived periodically). This ensures that the database can efficiently store parameters throughout the entire system operation process and meet the needs of rapid querying, data traceability, and correlation analysis during adaptive optimization of encoding parameters, providing accurate data support for stable system operation.

[0083] The target bitrate analysis module is connected to the encoding precision smoothing module, which in turn is connected to the encoding parameter adjustment module. All three modules are connected to the database.

[0084] The target bitrate analysis module is used to start the wireless transmission link monitoring thread after the Carplay system without wiring starts the reversing mode, collect the wireless transmission link performance indicators, and analyze the target bitrate of the reversing image frame.

[0085] The coding precision smoothing module is used by the reversing image acquisition device to acquire reversing image frames and perform spatial division to obtain several independent spatial coding blocks. Based on the target bit rate of the reversing image frame, the initial block-level coding precision matrix corresponding to the reversing image frame is analyzed. The coding precision of each spatial position on the initial block-level coding precision matrix is ​​smoothed between frames to form the final block-level coding precision matrix.

[0086] The encoding parameter adjustment module is used to perform differentiated encoding processing on each spatial encoding block based on the final block-level encoding precision matrix, generate an encoded data stream adapted to the performance of the current wireless transmission link, and transmit the reversing image frame to the Carplay terminal through the wireless transmission link. During the transmission process, the transmission status of the encoded data stream is monitored in real time, the monitoring results are obtained, and the encoding parameters are dynamically adjusted according to the monitoring results to achieve adaptive dynamic optimization of the encoding process in the scenario of wireless transmission of reversing images.

[0087] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0088] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0089] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A wireless transmission method for CarPlay reversing image without wiring, characterized in that, Includes the following steps: Step 1: After the CarPlay system without wiring starts in reversing mode, it starts the wireless transmission link monitoring thread to collect wireless transmission link performance indicators and analyze the target bit rate of the reversing image frame. Step 2: The reversing image acquisition device acquires reversing image frames and divides them spatially to obtain several independent spatial coding blocks. Based on the target bit rate of the reversing image frames, the initial block-level coding precision matrix corresponding to the reversing image frames is analyzed. Inter-frame smoothing operation is performed on the coding precision of each spatial position on the initial block-level coding precision matrix to form the final block-level coding precision matrix. Step 3: Based on the final block-level coding precision matrix, perform differentiated coding processing on each spatial coding block to generate a coded data stream adapted to the performance of the current wireless transmission link. Transmit the reversing image frame to the Carplay terminal through the wireless transmission link. During the transmission process, monitor the transmission status of the coded data stream in real time, obtain the monitoring results, and dynamically adjust the coding parameters according to the monitoring results to achieve adaptive dynamic optimization of the coding process in the wireless transmission scenario of reversing image.

2. The wireless transmission method for CarPlay reversing image without wiring as described in claim 1, characterized in that: The target bitrate of the reversing image frame was analyzed, and the specific analysis process is as follows: After smoothing the performance indicators of the wireless transmission link through an exponential weighting algorithm, the result is labeled as the link transmission quality coefficient. The link transmission quality coefficient characterizes the real-time transmission performance of the wireless transmission link in the scenario of wireless transmission of CarPlay reversing image without wiring. Using the link transmission quality coefficient as an index, the available bandwidth of the wireless transmission link is mapped from the database; Based on the available bandwidth of the wireless transmission link, the target bit rate of the reversing image frame is mapped by a piecewise linear function, and the variation range of the target bit rate between adjacent frames is limited.

3. The wireless transmission method for CarPlay reversing image without wiring as described in claim 1, characterized in that: The initial block-level coding precision matrix corresponding to the reversing image frame is analyzed, and the specific analysis process is as follows: Obtain the total bit budget for the wireless transmission link; The current reversing image frame is divided into several spatial coding blocks; The content complexity of each spatial coding block is analyzed, and each spatial coding block is assigned a corresponding importance weight. The regional priority score of each spatial coding block is then calculated by weighted summation. Based on the regional priority score of each spatial coding block, the total bit budget of the current wireless transmission link is allocated to obtain the block-level bit budget of each spatial coding block. The block-level bit budget of each spatial coding block is mapped to a preset matrix level index set to determine the block-level coding precision used by each spatial coding block; According to the spatial position of each spatial coding block in the current reversing image frame, the corresponding original matrix level is filled into a two-dimensional grid consistent with the coding block division, so as to obtain an initial block-level coding precision matrix that reflects the block-level coding precision of each spatial coding block in terms of spatial distribution.

4. The wireless transmission method for CarPlay reversing image without wiring as described in claim 1, characterized in that: The specific process of performing inter-frame smoothing operation on the coding precision of each spatial position on the initial block-level coding precision matrix is ​​as follows: For a spatial location on the initial block-level coding precision matrix, the block-level coding precision actually used at that spatial location in the previous frame is read as the historical precision. The block-level coding precision corresponding to the current reversing image frame at this spatial location is weighted and calculated with the historical precision according to a preset smoothing coefficient. The weighted result is marked as the temporary block-level coding precision at this spatial location. The smoothing coefficient is used to control the proportion of the current frame block-level coding precision and the historical precision of the previous frame in the weighted result; Obtain the precision deviation between the temporary block-level coding precision and the historical precision; Extract the first defined precision deviation value from the database; Based on the first defined precision deviation value, the precision deviation value between the temporary block-level coding precision and the historical precision is restricted, and the temporary block-level coding precision at each spatial location is updated sequentially to obtain the initial block-level coding precision matrix that satisfies the temporal continuity constraint.

5. The wireless transmission method for CarPlay reversing image without wiring as described in claim 4, characterized in that: The process of forming the final block-level coding precision matrix also includes performing a neighborhood space smoothing operation on the coding precision of each spatial position in the initial block-level coding precision matrix. The specific operation process is as follows: The second definition of the precision deviation value is obtained from the database; On the initial block-level coding precision matrix, analyze the precision deviation between the temporary block-level coding precision at a certain spatial location and the temporary block-level coding precision at adjacent spatial locations; If the accuracy deviation between the temporary block-level coding accuracy of this spatial location and the temporary block-level coding accuracy of the adjacent spatial locations is higher than the second defined accuracy deviation value, then the temporary block-level coding accuracy level of this spatial location is corrected so that it converges to the average value of the temporary block-level coding accuracy of the neighborhood, and the temporary block-level coding accuracy level of this spatial location is updated. The temporary block-level coding precision level of each spatial location is updated sequentially to form an initial block-level coding precision matrix that simultaneously satisfies the spatial continuity constraint.

6. The wireless transmission method for CarPlay reversing image without wiring as described in claim 4, characterized in that: The specific process for forming the final block-level coding precision matrix is ​​as follows: Perform inter-frame smoothing on the coding precision of each spatial position in the initial block-level coding precision matrix; After the inter-frame smoothing operation is completed, the initial block-level coding precision matrix is ​​updated; Perform neighborhood space smoothing operation on the coding precision of each spatial position on the updated initial block-level coding precision matrix; After the neighborhood space smoothing operation is completed, the initial block-level coding precision matrix is ​​updated, and the updated initial block-level coding precision matrix is ​​marked as the final block-level coding precision matrix.

7. The wireless transmission method for CarPlay reversing image without wiring as described in claim 1, characterized in that: The specific process for dynamically adjusting the coding parameters based on the monitoring results is as follows: The encoding parameters include a piecewise linear function, a first boundary precision deviation value, and a second boundary precision deviation value; Extract the actual output delay duration of the current reversing image frame from the monitoring results, and extract the defined output delay duration from the database; Compare the actual output delay of the current reversing image frame with the defined output delay. If the actual output delay of the current reversing image frame is not higher than the defined output delay, the reversing image encoding process will continue to be monitored. If the actual output delay of the current reversing image frame is higher than the defined output delay, then a loss value is added to the piecewise linear function to reduce the target bit rate. The reversing image encoding process is continuously monitored. Within the preset monitoring window, the duration of inter-frame smoothing operation and the total duration of neighborhood spatial smoothing operation are obtained and accumulated. The summation result is marked as the total loss duration. The encoding parameters are deeply adjusted based on the total loss time.

8. The wireless transmission method for CarPlay reversing image without wiring as described in claim 7, characterized in that: The process of deeply adjusting the coding parameters based on the total loss time is as follows: The total loss time is compared with the defined loss time stored in the database; If the total loss time does not exceed the defined loss time, the reversing image encoding process will be continuously monitored. If the total loss time is higher than the defined loss time, then the correction of the first definition accuracy deviation value and the second definition accuracy deviation value will be increased.

9. The wireless transmission method for CarPlay reversing image without wiring as described in claim 1, characterized in that: The adaptive dynamic optimization of the encoding process in the scenario of wireless transmission of reversing images also includes determining whether to update the encoding parameters. The specific determination process is as follows: If the encoding parameters are in an unupdateable state, then it is determined that the encoding parameters will not be updated; If the encoding parameters are in an updatable state, then obtain the total bitrate deviation value of this reversing image wireless transmission; Extract the total bitrate deviation value from the database and compare it with the total bitrate deviation value of this reversing image wireless transmission; If the total bitrate deviation of the current reversing image wireless transmission is higher than the defined total bitrate deviation, then it is determined that the encoding parameters will not be updated. If the total bitrate deviation of this reversing image wireless transmission is not higher than the defined total bitrate deviation, then it is determined that the encoding parameters should be updated, and the encoding parameters that are finally fixed during this reversing image wireless transmission process should be overwritten with the historical encoding parameters in the database.

10. A wireless CarPlay reversing image transmission system without wiring, employing the wireless CarPlay reversing image transmission method as described in any one of claims 1-9, characterized in that: include: Target bitrate analysis module, encoding precision smoothing module, and encoding parameter adjustment module; The target bitrate analysis module is used to start the wireless transmission link monitoring thread after the Carplay system without wiring starts the reversing mode, collect the wireless transmission link performance indicators, and analyze the target bitrate of the reversing image frame. The encoding precision smoothing module is used by the reversing image acquisition device to acquire reversing image frames, perform spatial division to obtain several independent spatial encoding blocks, analyze the initial block-level encoding precision matrix corresponding to the reversing image frame based on the target bit rate of the reversing image frame, and perform inter-frame smoothing operation on the encoding precision of each spatial position on the initial block-level encoding precision matrix to form the final block-level encoding precision matrix. The encoding parameter adjustment module is used to perform differentiated encoding processing on each spatial encoding block based on the final block-level encoding precision matrix, generate an encoded data stream adapted to the performance of the current wireless transmission link, and transmit the reversing image frame to the Carplay terminal through the wireless transmission link. During the transmission process, the transmission status of the encoded data stream is monitored in real time, the monitoring results are obtained, and the encoding parameters are dynamically adjusted according to the monitoring results to achieve adaptive dynamic optimization of the encoding process in the wireless transmission scenario of reversing image.

Images