The present application disclose a picture processing method and apparatus, which are applicable to a video picture processing scenario. The method includes: obtaining at least two transform coefficient blocks, where each of the at least two transform coefficient blocks includes at least two transform coefficients that correspond to different frequency points; determining, according to a preset frequency point scanning order, the transform coefficients that are in the at least two transform coefficient blocks and that correspond to a selected frequency point; and encoding, according to a preset coefficient scanning order, each transform coefficient corresponding to the selected frequency point, to obtain picture encoded information.

Methods and apparatus for video processing are disclosed. The processing may include video encoding, video decoding, or video transcoding. One example method includes performing a conversion between a current block of a video and a bitstream of the video. The bitstream conforms to a rule that specifies that a context index offset is used for including a first sign flag of a first coefficient in the bitstream. The rule specifies that a value of the context index offset is based on whether a first coding mode is applied on the current block in the bitstream.

This application provides a method for decoding an unmatched pixel performed by an electronic device. The method includes: obtaining a bit stream of a target video sequence and decoding the bit stream of the target video sequence by at least two entropy decoding methods, to obtain a binary symbol string of an unmatched pixel in a target picture block, the target picture block being obtained by dividing a target picture frame in the target video sequence; inversely binarizing the binary symbol string, to obtain a component value of the unmatched pixel; and obtaining the target picture block based on the component value of the unmatched pixel. This method can improve the flexibility of coding and facilitate equalization between the coding performance and the coding overhead of the unmatched pixel.

This application provides a method for decoding an unmatched pixel performed by an electronic device. The method includes: obtaining a bit stream of a target video sequence and decoding the bit stream of the target video sequence by at least two entropy decoding methods, to obtain a binary symbol string of an unmatched pixel in a target picture block, the target picture block being obtained by dividing a target picture frame in the target video sequence; inversely binarizing the binary symbol string, to obtain a component value of the unmatched pixel; and obtaining the target picture block based on the component value of the unmatched pixel. This method can improve the flexibility of coding and facilitate equalization between the coding performance and the coding overhead of the unmatched pixel.

Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.

Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.

Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.

Disclosed are methods and apparatuses for image data encoding/decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.

A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.

A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.