Presentation on theme: "JPEG DCT Quantization FDCT of 8x8 blocks. –Order in increasing spatial frequency (zigzag) Low frequencies have more shape information, get finer quantization."— Presentation transcript:
JPEG DCT Quantization FDCT of 8x8 blocks. –Order in increasing spatial frequency (zigzag) Low frequencies have more shape information, get finer quantization. High’s often very small so go to zero after quantizing –If source has 8-bit entries ( s in [-2 7, ), can show that quantized DCT needs at most 11 bits (c in [-2 10, ])
JPEG DCT Quantization –Quantize with single 64x64 table of divisors –Quantization table can be in file or reference to standard –Standard quantizer based on JND. – Note can have one quantizer table for each image component –See Wallace p 12.
JPEG DCT Intermediate Entropy Coding –Variable length code (Huffman): High occurrence symbols coded with fewer bits –Intermediate code: symbol pairs –symbol-1 chosen from table of symbols s i,j i is run length of zeros preceding quantized dct amplitude, j is length of huffman coding of the dct amplitude –i = 0…15, j= 1…10, and s 0,0 =‘EOB’ s 15,0 = ‘ZRL’ –symbol-2: Huffman encoding of dct amplitude –Finally, these 162 symbols are Huffman encoded.
JPEG components Y = 0.299R G B Cb = R G + 0.5B Cr = 0.5R G B Optionally subsample Cb, Cr – replace each pixel pair with its average. Not much loss of fidelity. Reduce data by 1/2*1/3+1/2*1/3 = 1/3 More shape info in achromatic than chromatic components. (Color vision poor at localization).
JPEG goodies Progressive mode - multiple scans, e.g. increasing spatial frequency so decoding gives shapes then detail Hierarchical encoding - multiple resolutions Lossless coding mode JFIF: –User embedded data –more than 3 components possible?
01 00 s1 01 s2 11 s3 100 s s s5 Huffman Encoding
01 00 s1 01 s2 11 s3 100 s s s Traverse from root to leaf, then repeat: s3 s5 s3 s2 s4 Huffman Encoding