Abstract: Methods and apparatus for parsing friendly and error resilient merge flag coding in video coding are provided. In some methods, in contrast to merging candidate list size dependent coding of the merge flag in the prior art, a merge flag is always encoded in the encoded bit stream for each inter-predicted prediction unit (PU) that is not encoded using skip mode. In some methods, in contrast to the prior art that allowed the merging candidate list to be empty, one or more zero motion vector merging candidates formatted according to the prediction type of the slice containing a PU are added to the merging candidate list if needed to ensure that the list is not empty and/or to ensure that the list contains a maximum number of merging candidates.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for GPU wave-to-wave optimization. A graphics processor may execute a shader program for a first wave associated with a draw call or a compute kernel. The graphics processor may identify at least one first indication for the first wave associated with the draw call or the compute kernel. The graphics processor may store the at least one first indication for the first wave to a memory location. The graphics processor may execute the shader program for at least one second wave associated with the draw call or the compute kernel. The execution of the shader program for the at least one second wave may be based on the shader program for the at least one second wave reading the memory location to retrieve the at least one first indication.

Abstract: Methods and apparatus for video processing are described. The video processing may include video encoding, video decoding or video transcoding. One example video processing method includes performing a conversion between a video including one or more pictures including one or more slices and a coded representation of the video, wherein the coded representation conforms to a format rule that specifies that a condition of a field in the coded representation controls a constraint on a slice type or whether the slice type is included in the coded representation for a video slice, wherein the field includes a general constraint flag, a network abstraction layer unit type or whether the video slice is in a first video picture of an access unit.

Abstract: Decompressing a compressed image to obtain a decompressed image includes receiving, in a compressed stream, compressed pixel values of the compressed image; decompressing, from the compressed stream, a first compressed pixel value of the compressed pixel values using a lossy floating-point decompression scheme to obtain a floating-point pixel value; rounding the floating-point pixel value to a nearest integer to obtain a pixel value of the decompressed image; and displaying or storing the decompressed image.

Abstract: A data analysis system to analyze data. The data analysis system includes a data buffer configured to receive data to be analyzed. The data analysis system also includes a state machine lattice. The state machine lattice includes multiple data analysis elements and each data analysis element includes multiple memory cells configured to analyze at least a portion of the data and to output a result of the analysis. The data analysis system includes a buffer interface configured to receive the data from the data buffer and to provide the data to the state machine lattice.

Abstract: A display apparatus and a controlling method thereof are disclosed. The display apparatus includes: a memory storing one or more instructions; and a processor configured to execute the stored one or more instructions to: obtain encoded data of a first digital image and artificial intelligence (AI) meta-information indicating a specification of a deep neural network (DNN), obtain a second digital image corresponding to the first digital image by decoding the encoded data, obtain a light signal converted from the second digital image according to a previously determined electro-optical transfer function (EOTF), and obtain a display signal by processing the light signal by using an opto-optical transfer function (OOTF) and a high dynamic range (HDR) DNN set according to the AI meta-information.

Abstract: It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.

Abstract: A display device includes a display panel and a driver which receives image signals and transmits data signals to the display panel. The driver includes an image sticking compensator that converts the image signals such that the first image is periodically shifted while being displayed. The image sticking compensator includes an extractor which extracts compensation area data corresponding to a first image displayed in a compensation area, a calculator which calculates fixed data based on the compensation area data and corresponding to the first image, and a shifter which generates shift-fixed data based on the fixed data. The compensation area includes a first area in which the first image is displayed and a second area in which a peripheral image at least partially surrounding the first image is displayed.

Abstract: The disclosure regards cross-component prediction and methods for deriving of a linear model for obtaining a first-component sample for a first-component block from an associated reconstructed second-component sample of a second-component block in the same frame, the method comprising determining the parameters of a linear equation representing a straight line passing through two points, each point being defined by two variables, the first variable corresponding to a second-component sample value, the second variable corresponding to a first-component sample value, based on reconstructed samples of both the first-component and the second-component; and deriving the linear model defined by the straight line parameters; wherein said determining the parameters uses integer arithmetic.

Abstract: A method of constraining data represented in a deep neural network is described. The method includes determining an initial shifting specified to convert a fixed-point input value to a floating-point output value. The method also includes determining an additional shifting specified to constrain a dynamic range during converting of the fixed-point input value to the floating-point output value. The method further includes performing both the initial shifting and the additional shifting together to form a dynamic, range constrained, normalized floating-point output value.

Abstract: A distributed event processing system is disclosed that receives a batch of events via a continuous data stream and performs the serialization of data in the batch of events. In certain embodiments, the system identifies a first data type of a first attribute for each event in a batch of events and determines a first type of data compression to be performed on data values represented by the first attribute. The system determines a first type of data compression to be performed on data values represented by the first attribute based on the first data type of the first attribute. The system then generates a first set of serialized data values for the first attribute. The system processes the first set of serialized data values against a set of one or more continuous queries to generate a first set of output events.

Abstract: An image sensor and an image processing system in which the image sensor includes a sensing unit configured to generate a plurality of images having different luminances with respect to a same object, a pre-processor configured to merge n images (n is a natural number equal to or greater than 2) except for at least one of the plurality of images to generate a merged image, and an interface circuit configured to output the at least one image and the merged image to an external processor.

Abstract: An encoding method is provided which includes receiving a plurality of images, obtaining values of elements in a portion of the images, sorting the elements according to different values of the elements, sorting the elements according to a number of occurrences of the different values and encoding the elements using a subset of the different values having corresponding numbers of occurrences that are higher than corresponding numbers of occurrences of other values. Examples also include a processing device and method for use with palette mode encoding in which the elements are a portion of pixels in images and the values are color values of the portion of pixels in the images.

Abstract: An image processing apparatus including at least one processor coupled to a memory, serving as a first obtainment unit to obtain a luminance value of image data corresponding to a first luminance range, a second obtainment unit to obtain information for a second luminance range that is less than the first luminance range, a classification unit to classify, based on a correspondence relationship of luminance value conversion from the first luminance range to the second luminance range, the first luminance range of the image data into a plurality of regions, and a display unit to cause a display device to display an image based on the image data such that a pixel of the image having a luminance value belonging to a region, in which a tone characteristic in the image data can be restored when the luminance value conversion is performed, can be specified in the displayed image.

Abstract: A method for image-processing and an electronic device are disclosed. The method includes: obtaining a RAW image packet comprising at least two RAW images, the at least two RAW images having different exposure durations; unpacking the RAW image packet and obtaining the at least two of RAW images; obtaining a High Dynamic Range (HDR) RAW image by performing an image synthesis operation on the at least two RAW images; and performing a previewing, photographing, or video-recording operation on the HDR RAW image.

Abstract: The invention discloses a point cloud geometric compression method based on a depth auto-encoder, which comprises the following steps of: (1) point cloud preprocessing: collecting a type of point cloud to be compressed as a training set, and normalizing the training set into a unit circle; (2) point cloud down-sampling: down-sampling the point clouds in the training set so that each point cloud have the same point number m and each point in the point cloud has (x, y, z) three-dimensional coordinates; and adopting a farthest point down-sampling method by randomly selecting a point for the first time and then selecting the point farthest away from the selected point set every time to add into the selected point set until the selected point number meets the requirement; (3) training a compression model: inputting the point cloud sampled in step (2) into a point cloud geometric compression framework based on a depth auto-encoder for training; and (4) geometric compression of the point cloud: applying the trained

Abstract: A data compression apparatus includes: a compression unit that performs a compression process on output data that is sequentially outputted as time series data by a monitoring apparatus for monitoring a monitoring target range; an output unit that outputs the output data on which the compression process is performed, to a data processing apparatus that performs an object detection process for detecting an object that exists in the monitoring target range by using the output data; and a setting unit that sets a compression ratio used in the compression unit, on the basis of an accuracy information indicating a relationship between a compression ratio at which the output data is compressed and a detection accuracy of the object by the object detection process performed by using the output data compressed at the compression ratio.

Abstract: An apparatus configured for head-worn by a user, includes: a screen configured to present graphics for the user; a camera system configured to view an environment in which the user is located; and a processing unit coupled to the camera system, the processing unit configured to: obtain a first image with a first resolution, the first image having a first corner, determine a second image with a second resolution, the second image having a second corner that corresponds with the first corner in the first image, wherein the second image is based on the first image, the second resolution being less than the first resolution, detect the second corner in the second image, determine a position of the second corner in the second image, and determine a position of the first corner in the first image based at least in part on the determined position of the second corner in the second image.

Abstract: A classifier training method includes detecting error data from training data; and training a classifier configured to detect an object based on the error data.

Abstract: This application provides a method for updating a route in a network. The first network device sends a first LSA packet to a third network device, so that the third network device generates a first route whose destination address is a second IP address, where a next-hop IP address of the first route is the IP address of the first network device, and the second IP address belongs to the IP network segment corresponding to the first IP address which is an IP address of the first network device. The first network device sends a second LSA packet to the third network device when determining that switching needs to be performed on a next hop of a route in the third network device, whose destination address belongs to the IP network segment, and whose next-hop IP address is the first IP address.

Abstract: The disclosure relates to the transfer of per-pixel transparency information using video codecs that do not provide an alpha channel (alternatively referred to as “transparency-agnostic video codecs”). For example, alpha information of visual elements may be transcoded into the supported channels of a video stream to generate additional samples of a supported color space, which are representative of the alpha information. After being encoded by a “transparency-agnostic video codec” and transmitted, the received alpha information may then be extracted from the supported channels of the video stream to render the received visuals with corresponding per-pixel transparency.

Abstract: Included are: an image acquiring unit acquiring a determination target image in which a target object to be inspected is captured; a learning result acquiring unit acquiring a result of machine learning of forward domain conversion of an image or inverse domain conversion of an image performed using a normal image in which the target object in a normal state is captured as training data; a determination target image analysis unit obtaining a domain-converted image by sequentially performing forward domain conversion and inverse domain conversion on the determination target image using the result of the machine learning; and a determination unit determining whether or not an abnormality is occurring in the target object captured in the determination target image by comparing the determination target image and the domain-converted image.

Abstract: The subject technology provides a messaging application in which an image can be captured and supplemented with supplemental content such as stickers, animations, etc., from within an active messaging thread. In this manner, a user participating in a messaging conversation with a remote user can add stickers, animations, and/or adaptive content to an image captured by the user, without having to locally cache/store the captured image before editing and without having to leave the messaging conversion (or the messaging application) to access an image editing application.

Abstract: An electronic apparatus includes: a storage and a processor. The processor is configured to: select a second pixel having a similarity to a first pixel equal to or greater than a threshold value among neighboring pixels based on a first chrominance signal of the first pixel and second chrominance signals of pixels neighboring the first pixel among a plurality of pixels included in an input image, identify index information corresponding to a pixel combination having a highest similarity to the first pixel based on a first luminance signal of the first pixel, a second luminance signal of the second pixel, and a plurality of pixel combinations with respect to the first pixel, and control the electronic apparatus to store the image by replacing a pixel value of the first pixel with the identified index information.

Abstract: Methods and systems for defocusing a rendered computer-generated image are presented. Pixel values for a pixel array are determined from a scene description. A blur amount for each pixel is determined based on a lens function representing a lens shape and/or effect. A blur amount and blur transparency value are determined for the pixel based on the lens function and pixel depth. A convolution range comprising pixels adjacent to the pixel is determined based on the blur amount. A blend color value is determined for the pixel based on the color value of the pixel, color values of pixels in the convolution range, and the blur transparency value. The blend color value is scaled based on the blend color value and a modified pixel color value is determined from scaled blend color values.

Abstract: An apparatus to facilitate encoding of point cloud data is disclosed. The apparatus includes one or more processors to receive point cloud data including a plurality of images and camera parameters, generate encoded point cloud data including a color texture image and a depth image having cropped regions for each of the plurality of images, and metadata to describe the camera parameters and a mapping of the cropped regions to a real coordinate space.

Abstract: A data output apparatus includes: a video decoder that decodes a video stream to generate a first video signal; an external metadata acquisition unit that acquires one or more pieces of metadata corresponding to one or more first conversion modes; an HDR metadata interpreter that interprets one of the one or more pieces of metadata to acquire characteristic data and conversion auxiliary data; a DR converter that supports one or more second conversion modes and performs conversion processing of a luminance range of the first video signal based on the conversion auxiliary data to generate a second video signal; and an HDMI output unit that outputs the second video signal to a display apparatus.

Abstract: Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

Abstract: The color coding approach adopted by the Anchor Point Cloud Compression (PCC) leads to a significant color distortion, even at very low compression ratios. This color distortion is also referred to as color leaking. Two approaches are able to mitigate the color leaking. Both approaches reduce the color leaking significantly, while one of them (e.g., 1D subsampling) is more robust and preferred in most cases. A more general approach is an adaptive selection between these two approaches.

Abstract: An image signal processing device of the present disclosure includes a luminance correction section that performs, on a basis of information on a maximum output luminance value in a display section, luminance correction on an image signal to be supplied to the display section, the maximum output luminance value being variable.

Abstract: A method and a device for processing a current pixel block of an image using a palette prediction mode according to HEVC Format Range Extension. The mode uses a current palette to build a predictor block of indexes to predict the current pixel block. The current palette comprises entries associating entry indexes with pixel values. The method comprises predicting the current palette from entries of two or more palettes that are palettes previously used to process blocks of pixels. A palette predictor may be used that is built from the two or more palettes, preferably from the last used palette for a coding unit in the current coding entity and from the previously used palette for which a flag bitmap indicates whether or not its elements have been copied into the last used palette. Accordingly, the coding of the palette mode is improved.

Abstract: A computer is caused to realize: a line drawing data acquisition function to acquire line drawing data to be colored; a size-reducing process function to perform a size-reducing process on the line drawing data acquired to a predetermined reduced size so as to obtain size-reduced line drawing data; a first coloring process function to perform a coloring process on the size-reduced line drawing data based on a first learned model that has previously learned the coloring process on the size-reduced line drawing data by using sample data; and a second coloring process function to perform a coloring process on original line drawing data by receiving an input of the original line drawing data and colored, size-reduced line drawing data as the size-reduced line drawing data on which the first coloring process function has performed the coloring, based on a second learned model that has previously learned the coloring process on the sample data by receiving an input of the sample data and colored, size-reduced sampl

Abstract: An artificial intelligence (AI) decoding apparatus includes at least one processor configured to execute one or more instructions to: obtain a second image corresponding to a first image by performing first decoding on image data included in a main bitstream, obtain AI upscaling activation flag information included in AI data of a sub-bitstream, determine whether to perform AI upscaling on the second image, based on the AI upscaling activation flag information, when it is determined that AI upscaling is to be performed on the second image, obtain a third image by performing AI upscaling on the second image, through an upscaling deep neural network (DNN) set according to upscaling DNN information, the upscaling DNN information selected from among a plurality of pieces of pre-stored upscaling DNN information based on at least a portion of the image data and/or at least a portion of AI sub-data.

Abstract: A region extraction apparatus provided with a candidate extraction unit, a categorization unit and a region extraction unit. The candidate extraction unit extracts a difference region where a first ground surface image photographed from above at a first time point and a second ground surface image photographed at a second time point are different. The categorization unit estimates a first category of a first object photographed in correspondence with a position in the first ground surface image, calculates a first categorization image indicating a relationship between the position of the first object and the first category, estimates a second category of a second object photographed in correspondence with a position in the second ground surface image and calculates a second categorization image indicating a relationship between the position of the second object and the second category. The region extraction unit extracts an extraction region from the difference region.

Abstract: The disclosure regards cross-component prediction and methods for deriving of a linear model for obtaining a first-component sample for a first-component block from an associated reconstructed second-component sample of a second-component block in the same frame, the method comprising determining the parameters of a linear equation representing a straight line passing through two points, each point being defined by two variables, the first variable corresponding to a second-component sample value, the second variable corresponding to a first-component sample value, based on reconstructed samples of both the first-component and the second-component; and deriving the linear model defined by the straight line parameters; wherein said determining the parameters uses integer arithmetic.

Abstract: Disclosed is a process for producing an animation of the multipage type, in which each still image of the animation has scalability properties. Preferably, each still image is to be saved on at least two levels, the first level containing few details and each following level providing additional details to the preceding level.

Abstract: Automation controls and associated text in images displayed by a computer application are automatically detected by way of region-based R-FCN and Faster R-CNN engines. Datasets comprising images containing application controls, where the application controls include images of application where width is greater than height, width is equal to height and height is greater than width are retrieved and each dataset is processed with the R-FCN and Faster R-CNN engines to generate a software robot configured to recognize corresponding application controls. Text is recognized by an optical character recognition system that employs a deep learning system trained to process a plurality of images to identify images representing text within each image and to convert the images representing text to textually encoded data. The deep learning system is trained with training data generated from a corpus of real-life text segments that are generated by a plurality of OCR modules.

Abstract: A computer is caused to realize: a line drawing data acquisition function to acquire line drawing data to be colored; a size-reducing process function to perform a size-reducing process on the line drawing data acquired to a predetermined reduced size so as to obtain size-reduced line drawing data; a first coloring process function to perform a coloring process on the size-reduced line drawing data based on a first learned model that has previously learned the coloring process on the size-reduced line drawing data by using sample data; and a second coloring process function to perform a coloring process on original line drawing data by receiving an input of the original line drawing data and colored, size-reduced line drawing data as the size-reduced line drawing data on which the first coloring process function has performed the coloring, based on a second learned model that has previously learned the coloring process on the sample data by receiving an input of the sample data and colored, size-reduced sampl

Abstract: A method of video decoding performed in a video decoder. The method including receiving a coded video bitstream including a current picture. The method further including, performing an inverse quantization on a current block included in the current picture. The method further including performing, after performing the inverse quantization, an inverse transform on the current block. The method further including performing a prediction process on the current block after performing the inverse transform. The method further including, after performing the prediction process on the current block, determining whether a predetermined condition is satisfied. The method further including, in response to determining that the predetermined condition is met, performing an inverse color transform on the current block.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating video frames using neural networks. One of the methods includes processing a sequence of video frames using an encoder neural network to generate an encoded representation; and generating a predicted next frame pixel by pixel according to a pixel order and a channel order, comprising: for each color channel of each pixel, providing as input to a decoder neural network (i) the encoded representation, (ii) color values for any pixels before the pixel in the pixel order, and (iii) color values for the pixel for any color channels before the color channel in the channel order, wherein the decoder neural network is configured to generate an output defining a score distribution over a plurality of possible color values, and determining the color value for the color channel of the pixel by sampling from the score distribution.

Abstract: Processing high dynamic range and or wide color gamut video data using a fixed-point implementation. A method of processing video data may include receiving one or more supplemental enhancement information (SEI) messages that contain information specifying how to determine parameters for performing an inverse dynamic range adjustment process, receiving decoded video data, and performing the inverse dynamic range adjustment process on the decoded video data using fixed-point computing in accordance with the information in the one or more SEI messages.

Abstract: A method and a determining device for determining a set of locations for a number of devices are disclosed. For each distance measure of multiple distance measures, the determining device generates a respective pair of clusters. The determining device determines information metrics. The determining device performs first actions. While a difference between a respective information metric and a further respective information metric is greater than a threshold, the determining device performs second actions, comprising replacing said replaceable pair with said weighted pair, replacing the respective information metric for said replaceable pair with said further respective information metric for said weighted pair, and performing said first actions. The determining device selects the set of locations based on the weighted pair of clusters. A corresponding computer program is also disclosed.

Abstract: An image capturing apparatus includes an image capturing unit configured to capture an image of an object and generate image data, a compression unit configured to compress the image data generated by the image capturing unit, a display control unit configured to decode the image data compressed by the compression unit and display the decoded image data on a display unit, and an instruction unit configured to issue an instruction to magnify an image to be displayed on the display unit, wherein, in a case where the instruction unit issues the instruction to magnify the image to be displayed on the display unit, the compression unit reduces a compression rate of the image data.

Abstract: In one embodiment, a method for determining the color for a sampling location may include using a computing system to determine a sampling location within a texture that comprises a plurality of texels. Each texel may encode a distance field indicating a distance between the texel and an edge depicted in the texture and an indicator indicating whether the texel is on a first predetermined side of the edge or a second predetermined side of the edge. The system may select, based on the sampling location, a set of texels in the plurality of texels to use to determine a color for the sampling location. The system may determine that the set of texels have indicators that are the same. The system may then determine, using the indicator of any texel in the set of texels, the color for the sampling location.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware sampling region for a hole-filling algorithm such as content-aware fill. Given a source image and a hole (or other target region to fill), a sampling region can be synthesized by identifying a band of pixels surrounding the hole, clustering these pixels based on one or more characteristics (e.g., color, x/y coordinates, depth, focus, etc.), passing each of the resulting clusters as foreground pixels to a segmentation algorithm, and unioning the resulting pixels to form the sampling region. The sampling region can be stored in a constraint mask and passed to a hole-filling algorithm such as content-aware fill to synthesize a fill for the hole (or other target region) from patches sampled from the synthesized sampling region.

Abstract: A system includes code including obtaining code to obtain a first syntax element that indicates a first quantization index value; at least one second syntax element that indicates an offset value, a second quantization index value for another coefficient by combining the first quantization index value and the offset value to obtain a combined value, and modifying the combined value to be a predetermined minimum value as the second quantization index value, fourth obtaining code to obtain a quantization step size that corresponds to the second quantization index value; and determining code to determine a mode in which the coded image is to be decoded based on determining whether the first quantization index value is equal to a quantization index value associated with lossless coding, and based on determining whether the offset value is less than or equal to the quantization index value associated with the lossless coding.

Abstract: A picture file processing method, apparatus, and computer readable medium are provided. The method includes parsing a picture file, to obtain a picture header information data segment, first stream data, and second stream data of a first image thereof. The first stream data of the first image is decoded to generate red-green-blue (RGB) data of the first image. The second stream data of the first image is decoded to generate Alpha data of the first image. Red-green-blue-alpha (RGBA) data corresponding to the first image are generated according to the RGB data and the Alpha data.

Abstract: One or more computing devices, systems, and/or methods for constructing a chat video are provided. For example, a user may define a participant definition defining one or more participants for a conversation to playback through the chat video. The user may define a sequence of content items (e.g., images, videos, text, emojis, emoticons, etc.) that are to be displayed through a chat interface for the conversation. A sequence of message elements (e.g., chat bubbles) are constructed based upon the participant definition and/or the sequence of content items. A message element comprises a content item and is assigned to a participant. The sequence of message elements are surfaced through the chat interface to visually present the conversation as a rendering used to construct the chat video.

Abstract: Video processing techniques and pipelines that support capture, distribution, and display of high dynamic range (HDR) image data to both HDR-enabled display devices and display devices that do not support HDR imaging. A sensor pipeline may generate standard dynamic range (SDR) data from HDR data captured by a sensor using tone mapping, for example local tone mapping. Information used to generate the SDR data may be provided to a display pipeline as metadata with the generated SDR data. If a target display does not support HDR imaging, the SDR data may be directly rendered by the display pipeline. If the target display does support HDR imaging, then an inverse mapping technique may be applied to the SDR data according to the metadata to render HDR data for display. Information used in performing color gamut mapping may also be provided in the metadata and used to recover clipped colors for display.

Abstract: The present invention is related to video coding and decoding, in particular HEVC RExt that define a palette coding mode dedicated to the coding of screen contents. In improved palette coding modes according to the invention, when building a palette, each time a new pixel is added to the class a palette entry defines, the palette entry is modified to take the means value of the pixels belonging to such class. In other improved palette coding modes, a built palette is post-processed to substitute a palette entry with a close entry of a palette predictor PRED. In yet other embodiments, palette coding modes having different threshold values to drive the building of their respective palettes are successively tested to use the best one in terms of rate-distortion criterion.